Methods and systems for an artificial intelligence alimentary professional support network for vibrant constitutional guidance

ABSTRACT

A system for an artificial intelligence alimentary professional support network for vibrant constitutional guidance includes a computing device. The system includes a diagnostic engine designed and configured to receive a biological extraction from a user and generate a diagnostic output based on the biological extraction. The system includes an advisor module designed and configured to receive a request for an advisory input, generate an advisory output using the request for an advisory input and the diagnostic output, and transmit the advisory output. The system includes an alimentary input module designed and configured to receive the advisory output, select an informed advisor alimentary professional client device as a function of the request for an advisory input, and transmit the at least an advisory output to the informed advisor alimentary professional client device.

RELATED APPLICATION DATA

This application is a continuation of U.S. patent application Ser. No.16/372,562, filed on Apr. 2, 2019 which is hereby incorporated byreference herein in its entirety.

FIELD OF THE INVENTION

The present invention generally relates to the field of artificialintelligence. In particular, the present invention is directed tomethods and systems for an artificial intelligence alimentaryprofessional support network for vibrant constitutional guidance.

BACKGROUND

Automated analysis of data and correct transmission of said data can bechallenging due to the complexity of and multiplicity of data to beanalyzed. Knowing which data should be transmitted to which user can behighly complex due to the unique and individual needs of each user—aproblem exacerbated by the burgeoning volume of data available foranalysis. Transmissions to incorrect professionals can lead toinaccuracies within systems, waste time trying to correct cumbersomeissues, and ultimately frustrate users.

Current alimentary professional support networks are limited toproviding alimentary suggestions and advice based on analyses derivedfrom user information and user inputs collected at a superficial level.This may result in inapplicability of alimentary support to the specificuser or a lack of continuous updating of information necessary tofurnish an effective alimentary professional support network inreal-time.

SUMMARY OF THE DISCLOSURE

In an aspect, a system for an artificial intelligence alimentaryprofessional support network for vibrant constitutional guidance, thesystem comprising a computing device. The system includes at least adiagnostic engine operating on the computing device, designed andconfigured to receive a first training data set including a plurality offirst data entries, each first data entry of the plurality of first dataentries including at least an element of physiological state data and atleast a correlated first prognostic label. The system is furtherconfigured to receive a second training data set including a pluralityof second data entries, each second data entry of the plurality ofsecond data entries including at least a second prognostic label and atleast a correlated ameliorative process label. The system is furtherconfigured to receive at least a biological extraction from a user. Thesystem is further configured to generate a diagnostic output based onthe first training set, the second training set, and the at least abiological extraction. The system includes an advisory module designedand configured to detect a nutritional advisory intervention event as afunction of the diagnostic output. The system is further configured togenerate a response to the advisory intervention event wherein theresponse identifies an advisory action. The system is further configuredto transmit the response to the user client device.

In an aspect, a method of an artificial intelligence alimentaryprofessional support network for vibrant constitutional guidance. Themethod includes receiving by a computing device a first training dataset including a plurality of first data entries, each first data entryof the plurality of first data entries including at least an element ofphysiological state data and at least a correlated first prognosticlabel. The method includes receiving by the computing device a secondtraining data set including a plurality of second data entries, eachsecond data entry of the plurality of second data entries including atleast a second prognostic label and at least a correlated ameliorativeprocess label. The method includes receiving by the computing device atleast a biological extraction from a user. The method includesgenerating by the computing device a diagnostic output based on thefirst training set, the second training set, and the at least abiological extraction. The method includes detecting by the computingdevice a nutritional advisory intervention event as a function of thediagnostic output. The method includes generating by the computingdevice a response to the advisory intervention event wherein theresponse identifies an advisory action. The method includes transmittingby the computing device the response to the user client device.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram illustrating an exemplary embodiment of asystem for utilizing diagnostics for informed vibrant constitutionalguidance;

FIG. 2 is a block diagram illustrating embodiments of data storagefacilities for use in disclosed systems and methods;

FIG. 3 is a block diagram illustrating an exemplary embodiment of abiological extraction database;

FIG. 4 is a block diagram illustrating an exemplary embodiment of anexpert knowledge database;

FIG. 5 is a block diagram illustrating an exemplary embodiment of aprognostic label database;

FIG. 6 is a block diagram illustrating an exemplary embodiment of anameliorative process label database;

FIG. 7 is a block diagram illustrating an exemplary embodiment of aprognostic label learner and associated system elements;

FIG. 8 is a block diagram illustrating an exemplary embodiment of anameliorative process label learner and associated system elements;

FIG. 9 is a block diagram illustrating an exemplary embodiment of a plangenerator module and associated system elements;

FIG. 10 is a block diagram illustrating an exemplary embodiment of aprognostic label classification database;

FIG. 11 is a block diagram illustrating an exemplary embodiment of anameliorative process label classification database;

FIG. 12 is a block diagram illustrating an exemplary embodiment of anarrative language database;

FIG. 13 is a block diagram illustrating an exemplary embodiment of animage database;

FIG. 14 is a block diagram illustrating an exemplary embodiment of auser database;

FIG. 15 is a block diagram illustrating an exemplary embodiment of anadvisory module and associated system elements;

FIG. 16 is a block diagram illustrating an exemplary embodiment of anartificial intelligence advisor and associated system elements;

FIG. 17 is a block diagram illustrating an exemplary embodiment of anadvisory database;

FIG. 18 is a block diagram illustrating an exemplary embodiment of adefault response database;

FIG. 19 is a block diagram illustrating an exemplary embodiment of analimentary module and associated system elements;

FIG. 20 is a block diagram illustrating an exemplary embodiment of analimentary informed advisor selector database;

FIG. 21 is a block diagram illustrating an exemplary embodiment of auser requested database;

FIG. 22 is a flow diagram illustrating an exemplary embodiment of amethod of an artificial intelligence alimentary professional supportnetwork for vibrant constitutional guidance;

FIG. 23 is a flow diagram illustrating an exemplary embodiment of amethod of an artificial intelligence alimentary professional supportnetwork for vibrant constitutional guidance; and

FIG. 24 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

Turning now to FIG. 1, a system 100 for an artificial intelligencealimentary professional support network for vibrant constitutionalguidance is illustrated. Turning now to FIG. 1, an artificialintelligence advisory system 100 for vibrant constitutional guidance.Artificial intelligence advisory system includes a computing device 104.A computing device 104 may include any computing device as described inthis disclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. A computing device 104 may behoused with, may be incorporated in, or may incorporate one or moresensors of at least a sensor. Computing device may include, be includedin, and/or communicate with a mobile device such as a mobile telephoneor smartphone. A computing device 104 may include a single computingdevice operating independently, or may include two or more computingdevice operating in concert, in parallel, sequentially or the like; twoor more computing devices may be included together in a single computingdevice or in two or more computing devices. A computing device 104 withone or more additional devices as described below in further detail viaa network interface device. Network interface device may be utilized forconnecting a computing device 104 to one or more of a variety ofnetworks, and one or more devices. Examples of a network interfacedevice include, but are not limited to, a network interface card (e.g.,a mobile network interface card, a LAN card), a modem, and anycombination thereof. Examples of a network include, but are not limitedto, a wide area network (e.g., the Internet, an enterprise network), alocal area network (e.g., a network associated with an office, abuilding, a campus or other relatively small geographic space), atelephone network, a data network associated with a telephone/voiceprovider (e.g., a mobile communications provider data and/or voicenetwork), a direct connection between two computing devices, and anycombinations thereof. A network may employ a wired and/or a wirelessmode of communication. In general, any network topology may be used.Information (e.g., data, software etc.) may be communicated to and/orfrom a computer and/or a computing device. A computing device 104 mayinclude but is not limited to, for example, a computing device 104 orcluster of computing devices in a first location and a second computingdevice or cluster of computing devices in a second location. A computingdevice 104 may include one or more computing devices dedicated to datastorage, security, distribution of traffic for load balancing, and thelike. A computing device 104 may distribute one or more computing tasksas described below across a plurality of computing devices of computingdevice, which may operate in parallel, in series, redundantly, or in anyother manner used for distribution of tasks or memory between computingdevices. A computing device 104 may be implemented using a “sharednothing” architecture in which data is cached at the worker, in anembodiment, this may enable scalability of system 100 and/or computingdevice.

Still referring to FIG. 1, system 100 includes a diagnostic engine 108operating on the a computing device 104, wherein the diagnostic engine108 configured to receive a first training set including a plurality offirst data entries, each first data entry of the plurality of first dataentries including at least an element of physiological state data and atleast a correlated first prognostic label; receive a second training setincluding a plurality of second data entries, each second data entry ofthe plurality of second data entries including at least a secondprognostic label and at least a correlated ameliorative process label;receive at least a biological extraction from a user; and generate adiagnostic output based on the at least a biological extraction, thediagnostic output including at least a prognostic label and at least anameliorative process label using the first training set, the secondtraining set, and the at least a biological extraction. A computingdevice 104, diagnostic engine 108, and/or one or more modules operatingthereon may be designed and/or configured to perform any method, methodstep, or sequence of method steps in any embodiment described in thisdisclosure, in any order and with any degree of repetition. Forinstance, a computing device 104 and/or diagnostic engine 108 may beconfigured to perform a single step or sequence repeatedly until adesired or commanded outcome is achieved; repetition of a step or asequence of steps may be performed iteratively and/or recursively usingoutputs of previous repetitions as inputs to subsequent repetitions,aggregating inputs and/or outputs of repetitions to produce an aggregateresult, reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. A computing device 104and/or diagnostic engine 108 may perform any step or sequence of stepsas described in this disclosure in parallel, such as simultaneouslyand/or substantially simultaneously performing a step two or more timesusing two or more parallel threads, processor cores, or the like;division of tasks between parallel threads and/or processes may beperformed according to any protocol suitable for division of tasksbetween iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Continuing to refer to FIG. 1, diagnostic engine 108 may be designed andconfigured to receive training data. Training data, as used herein, isdata containing correlation that a machine-learning process may use tomodel relationships between two or more categories of data elements. Forinstance, and without limitation, training data may include a pluralityof data entries, each entry representing a set of data elements thatwere recorded, received, and/or generated together; data elements may becorrelated by shared existence in a given data entry, by proximity in agiven data entry, or the like. Multiple data entries in training datamay evince one or more trends in correlations between categories of dataelements; for instance, and without limitation, a higher value of afirst data element belonging to a first category of data element maytend to correlate to a higher value of a second data element belongingto a second category of data element, indicating a possible proportionalor other mathematical relationship linking values belonging to the twocategories. Multiple categories of data elements may be related intraining data according to various correlations; correlations mayindicate causative and/or predictive links between categories of dataelements, which may be modeled as relationships such as mathematicalrelationships by machine-learning processes as described in furtherdetail below. Training data may be formatted and/or organized bycategories of data elements, for instance by associating data elementswith one or more descriptors corresponding to categories of dataelements. As a non-limiting example, training data may include dataentered in standardized forms by persons or processes, such that entryof a given data element in a given field in a form may be mapped to oneor more descriptors of categories. Elements in training data may belinked to descriptors of categories by tags, tokens, or other dataelements; for instance, and without limitation, training data may beprovided in fixed-length formats, formats linking positions of data tocategories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),enabling processes or devices to detect categories of data.

Alternatively or additionally, and still referring to FIG. 1, trainingdata may include one or more elements that are not categorized; that is,training data may not be formatted or contain descriptors for someelements of data. Machine-learning algorithms and/or other processes maysort training data according to one or more categorizations using, forinstance, natural language processing algorithms, tokenization,detection of correlated values in raw data and the like; categories maybe generated using correlation and/or other processing algorithms. As anon-limiting example, in a corpus of text, phrases making up a number“n” of compound words, such as nouns modified by other nouns, may beidentified according to a statistically significant prevalence ofn-grams containing such words in a particular order; such an n-gram maybe categorized as an element of language such as a “word” to be trackedsimilarly to single words, generating a new category as a result ofstatistical analysis. Similarly, in a data entry including some textualdata, a person's name and/or a description of a medical condition ortherapy may be identified by reference to a list, dictionary, or othercompendium of terms, permitting ad-hoc categorization bymachine-learning algorithms, and/or automated association of data in thedata entry with descriptors or into a given format. The ability tocategorize data entries automatedly may enable the same training data tobe made applicable for two or more distinct machine-learning algorithmsas described in further detail below.

Still referring to FIG. 1, diagnostic engine 108 may be configured toreceive a first training set 112 including a plurality of first dataentries, each first data entry of the first training set 112 includingat least an element of physiological state data 116 and at least acorrelated first prognostic label 120. At least an element ofphysiological state data 116 may include any data indicative of aperson's physiological state; physiological state may be evaluated withregard to one or more measures of health of a person's body, one or moresystems within a person's body such as a circulatory system, a digestivesystem, a nervous system, or the like, one or more organs within aperson's body, and/or any other subdivision of a person's body usefulfor diagnostic or prognostic purposes. Physiological state data 116 mayinclude, without limitation, hematological data, such as red blood cellcount, which may include a total number of red blood cells in a person'sblood and/or in a blood sample, hemoglobin levels, hematocritrepresenting a percentage of blood in a person and/or sample that iscomposed of red blood cells, mean corpuscular volume, which may be anestimate of the average red blood cell size, mean corpuscularhemoglobin, which may measure average weight of hemoglobin per red bloodcell, mean corpuscular hemoglobin concentration, which may measure anaverage concentration of hemoglobin in red blood cells, platelet count,mean platelet volume which may measure the average size of platelets,red blood cell distribution width, which measures variation in red bloodcell size, absolute neutrophils, which measures the number of neutrophilwhite blood cells, absolute quantities of lymphocytes such as B-cells,T-cells, Natural Killer Cells, and the like, absolute numbers ofmonocytes including macrophage precursors, absolute numbers ofeosinophils, and/or absolute counts of basophils. Physiological statedata 116 may include, without limitation, immune function data such asInterleukine-6 (IL-6), TNF-alpha, systemic inflammatory cytokines, andthe like.

Continuing to refer to FIG. 1, physiological state data 116 may include,without limitation, data describing blood-born lipids, including totalcholesterol levels, high-density lipoprotein (HDL) cholesterol levels,low-density lipoprotein (LDL) cholesterol levels, very low-densitylipoprotein (VLDL) cholesterol levels, levels of triglycerides, and/orany other quantity of any blood-born lipid or lipid-containingsubstance. Physiological state data 116 may include measures of glucosemetabolism such as fasting glucose levels and/or hemoglobin A1-C (HbA1c)levels. Physiological state data 116 may include, without limitation,one or more measures associated with endocrine function, such as withoutlimitation, quantities of dehydroepiandrosterone (DHEAS), DHEA-Sulfate,quantities of cortisol, ratio of DHEAS to cortisol, quantities oftestosterone quantities of estrogen, quantities of growth hormone (GH),insulin-like growth factor 1 (IGF-1), quantities of adipokines such asadiponectin, leptin, and/or ghrelin, quantities of somatostatin,progesterone, or the like. Physiological state data 116 may includemeasures of estimated glomerular filtration rate (eGFR). Physiologicalstate data 116 may include quantities of C-reactive protein, estradiol,ferritin, folate, homocysteine, prostate-specific Ag,thyroid-stimulating hormone, vitamin D, 25 hydroxy, blood urea nitrogen,creatinine, sodium, potassium, chloride, carbon dioxide, uric acid,albumin, globulin, calcium, phosphorus, alkaline phosphatase, alanineamino transferase, aspartate amino transferase, lactate dehydrogenase(LDH), bilirubin, gamma-glutamyl transferase (GGT), iron, and/or totaliron binding capacity (TIBC), or the like. Physiological state data 116may include antinuclear antibody levels. Physiological state data 116may include aluminum levels. Physiological state data 116 may includearsenic levels. Physiological state data 116 may include levels offibrinogen, plasma cystatin C, and/or brain natriuretic peptide.

Continuing to refer to FIG. 1, physiological state data 116 may includemeasures of lung function such as forced expiratory volume, one second(FEV-1) which measures how much air can be exhaled in one secondfollowing a deep inhalation, forced vital capacity (FVC), which measuresthe volume of air that may be contained in the lungs. Physiologicalstate data 116 may include a measurement blood pressure, includingwithout limitation systolic and diastolic blood pressure. Physiologicalstate data 116 may include a measure of waist circumference.Physiological state data 116 may include body mass index (BMI).Physiological state data 116 may include one or more measures of bonemass and/or density such as dual-energy x-ray absorptiometry.Physiological state data 116 may include one or more measures of musclemass. Physiological state data 116 may include one or more measures ofphysical capability such as without limitation measures of gripstrength, evaluations of standing balance, evaluations of gait speed,pegboard tests, timed up and go tests, and/or chair rising tests.

Still viewing FIG. 1, physiological state data 116 may include one ormore measures of cognitive function, including without limitation Reyauditory verbal learning test results, California verbal learning testresults, NIH toolbox picture sequence memory test, Digital symbol codingevaluations, and/or Verbal fluency evaluations. Physiological state data204 may include one or more measures of psychological function or state,such as without limitation clinical interviews, assessments ofintellectual functioning and/or intelligence quotient (IQ) tests,personality assessments, and/or behavioral assessments. Physiologicalstate data 204 may include one or more psychological self-assessments,which may include any self-administered and/or automatedlycomputer-administered assessments, whether administered within system100 and/or via a third-party service or platform.

With continued reference to FIG. 1, physiological state data 116 mayinclude one or more evaluations of sensory ability, including measuresof audition, vision, olfaction, gustation, vestibular function and pain.Physiological state data 116 may include genomic data, includingdeoxyribonucleic acid (DNA) samples and/or sequences, such as withoutlimitation DNA sequences contained in one or more chromosomes in humancells. Genomic data may include, without limitation, ribonucleic acid(RNA) samples and/or sequences, such as samples and/or sequences ofmessenger RNA (mRNA) or the like taken from human cells. Genetic datamay include telomere lengths. Genomic data may include epigenetic dataincluding data describing one or more states of methylation of geneticmaterial. Physiological state data 116 may include proteomic data, whichas used herein, is data describing all proteins produced and/or modifiedby an organism, colony of organisms, or system of organisms, and/or asubset thereof. Physiological state data 116 may include data concerninga microbiome of a person, which as used herein, includes any datadescribing any microorganism and/or combination of microorganisms livingon or within a person, including without limitation biomarkers, genomicdata, proteomic data, and/or any other metabolic or biochemical datauseful for analysis of the effect of such microorganisms on otherphysiological state data 116 of a person, and/or on prognostic labelsand/or ameliorative processes as described in further detail below.Physiological state data 116 may include any physiological state data116, as described above, describing any multicellular organism living inor on a person including any parasitic and/or symbiotic organisms livingin or on the persons; non-limiting examples may include mites,nematodes, flatworms, or the like. Examples of physiological state data116 described in this disclosure are presented for illustrative purposesonly and are not meant to be exhaustive. Persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variousadditional examples of physiological state data 116 that may be usedconsistently with descriptions of systems and methods as provided inthis disclosure.

Continuing to refer to FIG. 1, each element of first training set 112includes at least a first prognostic label 120. A prognostic label, asdescribed herein, is an element of data identifying and/or describing acurrent, incipient, or probable future medical condition affecting aperson; medical condition may include a particular disease, one or moresymptoms associated with a syndrome, a syndrome, and/or any othermeasure of current or future health and/or healthy aging. At least aprognostic label may be associated with a physical and/or somaticcondition, a mental condition such as a mental illness, neurosis, or thelike, or any other condition affecting human health that may beassociated with one or more elements of physiological state data 116 asdescribed in further detail below. Conditions associated with prognosticlabels may include, without limitation one or more diseases, defined forpurposes herein as conditions that negatively affect structure and/orfunction of part or all of an organism. Conditions associated withprognostic labels may include, without limitation, acute or chronicinfections, including without limitation infections by bacteria,archaea, viruses, viroids, prions, single-celled eukaryotic organismssuch as amoeba, paramecia, trypanosomes, plasmodia, leishmania, and/orfungi, and/or multicellular parasites such as nematodes, arthropods,fungi, or the like. Prognostic labels may be associated with one or moreimmune disorders, including without limitation immunodeficiencies and/orauto-immune conditions. Prognostic labels may be associated with one ormore metabolic disorders. Prognostic labels may be associated with oneor more endocrinal disorders. Prognostic labels may be associated withone or more cardiovascular disorders. Prognostic labels may beassociated with one or more respiratory disorders. Prognostic labels maybe associated with one or more disorders affecting connective tissue.Prognostic labels may be associated with one or more digestivedisorders. Prognostic labels may be associated with one or moreneurological disorders such as neuromuscular disorders, dementia, or thelike. Prognostic labels may be associated with one or more disorders ofthe excretory system, including without limitation nephrologicaldisorders. Prognostic labels may be associated with one or more liverdisorders. Prognostic labels may be associated with one or moredisorders of the bones such as osteoporosis. Prognostic labels may beassociated with one or more disorders affecting joints, such asosteoarthritis, gout, and/or rheumatoid arthritis. Prognostic labels beassociated with one or more cancers, including without limitationcarcinomas, lymphomas, leukemias, germ cell tumor cancers, blastomas,and/or sarcomas. Prognostic labels may include descriptors of latent,dormant, and/or apparent disorders, diseases, and/or conditions.Prognostic labels may include descriptors of conditions for which aperson may have a higher than average probability of development, suchas a condition for which a person may have a “risk factor”; forinstance, a person currently suffering from abdominal obesity may have ahigher than average probability of developing type II diabetes. Theabove-described examples are presented for illustrative purposes onlyand are not intended to be exhaustive. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variousadditional examples of conditions that may be associated with prognosticlabels as described in this disclosure.

Still referring to FIG. 1, at least a prognostic label may be stored inany suitable data and/or data type. For instance, and withoutlimitation, at least a prognostic label may include textual data, suchas numerical, character, and/or string data. Textual data may include astandardized name and/or code for a disease, disorder, or the like;codes may include diagnostic codes and/or diagnosis codes, which mayinclude without limitation codes used in diagnosis classificationsystems such as The International Statistical Classification of Diseasesand Related Health Problems (ICD). In general, there is no limitation onforms textual data or non-textual data used as at least a prognosticlabel may take; persons skilled in the art, upon reviewing the entiretyof this disclosure, will be aware of various forms which may be suitablefor use as at least a prognostic label consistently with thisdisclosure.

With continued reference to FIG. 1, in each first data element of firsttraining set 112, at least a first prognostic label 120 of the dataelement is correlated with at least an element of physiological statedata 116 of the data element. In an embodiment, an element ofphysiological data is correlated with a prognostic label where theelement of physiological data is located in the same data element and/orportion of data element as the prognostic label; for example, andwithout limitation, an element of physiological data is correlated witha prognostic element where both element of physiological data andprognostic element are contained within the same first data element ofthe first training set 112. As a further example, an element ofphysiological data is correlated with a prognostic element where bothshare a category label as described in further detail below, where eachis within a certain distance of the other within an ordered collectionof data in data element, or the like. Still further, an element ofphysiological data may be correlated with a prognostic label where theelement of physiological data and the prognostic label share an origin,such as being data that was collected with regard to a single person orthe like. In an embodiment, a first datum may be more closely correlatedwith a second datum in the same data element than with a third datumcontained in the same data element; for instance, the first element andthe second element may be closer to each other in an ordered set of datathan either is to the third element, the first element and secondelement may be contained in the same subdivision and/or section of datawhile the third element is in a different subdivision and/or section ofdata, or the like. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various forms and/ordegrees of correlation between physiological data and prognostic labelsthat may exist in first training set 112 and/or first data elementconsistently with this disclosure.

In an embodiment, and still referring to FIG. 1, diagnostic engine 108may be designed and configured to associate at least an element ofphysiological state data 116 with at least a category from a list ofsignificant categories of physiological state data 116. Significantcategories of physiological state data 116 may include labels and/ordescriptors describing types of physiological state data 116 that areidentified as being of high relevance in identifying prognostic labels.As a non-limiting example, one or more categories may identifysignificant categories of physiological state data 116 based on degreeof diagnostic relevance to one or more impactful conditions and/orwithin one or more medical or public health fields. For instance, andwithout limitation, a particular set of biomarkers, test results, and/orbiochemical information may be recognized in a given medical field asuseful for identifying various disease conditions or prognoses within arelevant field. As a non-limiting example, and without limitation,physiological data describing red blood cells, such as red blood cellcount, hemoglobin levels, hematocrit, mean corpuscular volume, meancorpuscular hemoglobin, and/or mean corpuscular hemoglobin concentrationmay be recognized as useful for identifying various conditions such asdehydration, high testosterone, nutrient deficiencies, kidneydysfunction, chronic inflammation, anemia, and/or blood loss. As anadditional example, hemoglobin levels may be useful for identifyingelevated testosterone, poor oxygen deliverability, thiamin deficiency,insulin resistance, anemia, liver disease, hypothyroidism, argininedeficiency, protein deficiency, inflammation, and/or nutrientdeficiencies. In a further non-limiting example, hematocrit may beuseful for identifying dehydration, elevated testosterone, poor oxygendeliverability, thiamin deficiency, insulin resistance, anemia, liverdisease, hypothyroidism, arginine deficiency, protein deficiency,inflammation, and/or nutrient deficiencies. Similarly, measures of lipidlevels in blood, such as total cholesterol, HDL, LDL, VLDL,triglycerides, LDL-C and/or HDL-C may be recognized as useful inidentifying conditions such as poor thyroid function, insulinresistance, blood glucose dysregulation, magnesium deficiency,dehydration, kidney disease, familial hypercholesterolemia, liverdysfunction, oxidative stress, inflammation, malabsorption, anemia,alcohol abuse, diabetes, hypercholesterolemia, coronary artery disease,atherosclerosis, or the like. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various additionalcategories of physiological data that may be used consistently with thisdisclosure.

Still referring to FIG. 1, diagnostic engine 108 may receive the list ofsignificant categories according to any suitable process; for instance,and without limitation, diagnostic engine 108 may receive the list ofsignificant categories from at least an expert. In an embodiment,diagnostic engine 108 and/or a user device connected to diagnosticengine 108 may provide a graphical user interface, which may includewithout limitation a form or other graphical element having data entryfields, wherein one or more experts, including without limitationclinical and/or scientific experts, may enter information describing oneor more categories of physiological data that the experts consider to besignificant or useful for detection of conditions; fields in graphicaluser interface may provide options describing previously identifiedcategories, which may include a comprehensive or near-comprehensive listof types of physiological data detectable using known or recordedtesting methods, for instance in “drop-down” lists, where experts may beable to select one or more entries to indicate their usefulness and/orsignificance in the opinion of the experts. Fields may include free-formentry fields such as text-entry fields where an expert may be able totype or otherwise enter text, enabling expert to propose or suggestcategories not currently recorded. Graphical user interface or the likemay include fields corresponding to prognostic labels, where experts mayenter data describing prognostic labels and/or categories of prognosticlabels the experts consider related to entered categories ofphysiological data; for instance, such fields may include drop-downlists or other pre-populated data entry fields listing currentlyrecorded prognostic labels, and which may be comprehensive, permittingeach expert to select a prognostic label and/or a plurality ofprognostic labels the expert believes to be predicted and/or associatedwith each category of physiological data selected by the expert. Fieldsfor entry of prognostic labels and/or categories of prognostic labelsmay include free-form data entry fields such as text entry fields; asdescribed above, examiners may enter data not presented in pre-populateddata fields in the free-form data entry fields. Alternatively oradditionally, fields for entry of prognostic labels may enable an expertto select and/or enter information describing or linked to a category ofprognostic label that the expert considers significant, wheresignificance may indicate likely impact on longevity, mortality, qualityof life, or the like as described in further detail below. Graphicaluser interface may provide an expert with a field in which to indicate areference to a document describing significant categories ofphysiological data, relationships of such categories to prognosticlabels, and/or significant categories of prognostic labels. Any datadescribed above may alternatively or additionally be received fromexperts similarly organized in paper form, which may be captured andentered into data in a similar way, or in a textual form such as aportable document file (PDF) with examiner entries, or the like

Still referring to FIG. 1, diagnostic engine 108 may receive the list ofsignificant categories according to any suitable process; for instance,and without limitation, diagnostic engine 108 may receive the list ofsignificant categories from at least an expert. In an embodiment,diagnostic engine 108 and/or a user device connected to diagnosticengine 108 may provide a graphical user interface 124, which may includewithout limitation a form or other graphical element having data entryfields, wherein one or more experts, including without limitationclinical and/or scientific experts, may enter information describing oneor more categories of physiological data that the experts consider to besignificant or useful for detection of conditions; fields in graphicaluser interface may provide options describing previously identifiedcategories, which may include a comprehensive or near-comprehensive listof types of physiological data detectable using known or recordedtesting methods, for instance in “drop-down” lists, where experts may beable to select one or more entries to indicate their usefulness and/orsignificance in the opinion of the experts. Fields may include free-formentry fields such as text-entry fields where an expert may be able totype or otherwise enter text, enabling expert to propose or suggestcategories not currently recorded. Graphical user interface 124 or thelike may include fields corresponding to prognostic labels, whereexperts may enter data describing prognostic labels and/or categories ofprognostic labels the experts consider related to entered categories ofphysiological data; for instance, such fields may include drop-downlists or other pre-populated data entry fields listing currentlyrecorded prognostic labels, and which may be comprehensive, permittingeach expert to select a prognostic label and/or a plurality ofprognostic labels the expert believes to be predicted and/or associatedwith each category of physiological data selected by the expert. Fieldsfor entry of prognostic labels and/or categories of prognostic labelsmay include free-form data entry fields such as text entry fields; asdescribed above, examiners may enter data not presented in pre-populateddata fields in the free-form data entry fields. Alternatively oradditionally, fields for entry of prognostic labels may enable an expertto select and/or enter information describing or linked to a category ofprognostic label that the expert considers significant, wheresignificance may indicate likely impact on longevity, mortality, qualityof life, or the like as described in further detail below. Graphicaluser interface 124 may provide an expert with a field in which toindicate a reference to a document describing significant categories ofphysiological data, relationships of such categories to prognosticlabels, and/or significant categories of prognostic labels. Any datadescribed above may alternatively or additionally be received fromexperts similarly organized in paper form, which may be captured andentered into data in a similar way, or in a textual form such as aportable document file (PDF) with examiner entries, or the like

Referring again to FIG. 1, data information describing significantcategories of physiological data, relationships of such categories toprognostic labels, and/or significant categories of prognostic labelsmay alternatively or additionally be extracted from one or moredocuments using a language processing module 128. Language processingmodule 128 may include any hardware and/or software module. Languageprocessing module 128 may be configured to extract, from the one or moredocuments, one or more words. One or more words may include, withoutlimitation, strings of one or characters, including without limitationany sequence or sequences of letters, numbers, punctuation, diacriticmarks, engineering symbols, geometric dimensioning and tolerancing(GD&T) symbols, chemical symbols and formulas, spaces, whitespace, andother symbols, including any symbols usable as textual data as describedabove. Textual data may be parsed into tokens, which may include asimple word (sequence of letters separated by whitespace) or moregenerally a sequence of characters as described previously. The term“token,” as used herein, refers to any smaller, individual groupings oftext from a larger source of text; tokens may be broken up by word, pairof words, sentence, or other delimitation. These tokens may in turn beparsed in various ways. Textual data may be parsed into words orsequences of words, which may be considered words as well. Textual datamay be parsed into “n-grams”, where all sequences of n consecutivecharacters are considered. Any or all possible sequences of tokens orwords may be stored as “chains”, for example for use as a Markov chainor Hidden Markov Model.

Still referring to FIG. 1, language processing module 128 may compareextracted words to categories of physiological data recorded atdiagnostic engine 108, one or more prognostic labels recorded atdiagnostic engine 108, and/or one or more categories of prognosticlabels recorded at diagnostic engine 108; such data for comparison maybe entered on diagnostic engine 108 as described above using expert datainputs or the like. In an embodiment, one or more categories may beenumerated, to find total count of mentions in such documents.Alternatively or additionally, language processing module 128 mayoperate to produce a language processing model. Language processingmodel may include a program automatically generated by diagnostic engine108 and/or language processing module 128 to produce associationsbetween one or more words extracted from at least a document and detectassociations, including without limitation mathematical associations,between such words, and/or associations of extracted words withcategories of physiological data, relationships of such categories toprognostic labels, and/or categories of prognostic labels. Associationsbetween language elements, where language elements include for purposesherein extracted words, categories of physiological data, relationshipsof such categories to prognostic labels, and/or categories of prognosticlabels may include, without limitation, mathematical associations,including without limitation statistical correlations between anylanguage element and any other language element and/or languageelements. Statistical correlations and/or mathematical associations mayinclude probabilistic formulas or relationships indicating, forinstance, a likelihood that a given extracted word indicates a givencategory of physiological data, a given relationship of such categoriesto prognostic labels, and/or a given category of prognostic labels. As afurther example, statistical correlations and/or mathematicalassociations may include probabilistic formulas or relationshipsindicating a positive and/or negative association between at least anextracted word and/or a given category of physiological data, a givenrelationship of such categories to prognostic labels, and/or a givencategory of prognostic labels; positive or negative indication mayinclude an indication that a given document is or is not indicating acategory of physiological data, relationship of such category toprognostic labels, and/or category of prognostic labels is or is notsignificant. For instance, and without limitation, a negative indicationmay be determined from a phrase such as “telomere length was not foundto be an accurate predictor of overall longevity,” whereas a positiveindication may be determined from a phrase such as “telomere length wasfound to be an accurate predictor of dementia,” as an illustrativeexample; whether a phrase, sentence, word, or other textual element in adocument or corpus of documents constitutes a positive or negativeindicator may be determined, in an embodiment, by mathematicalassociations between detected words, comparisons to phrases and/or wordsindicating positive and/or negative indicators that are stored in memoryat diagnostic engine 108, or the like.

Still referring to FIG. 1, language processing module 128 and/ordiagnostic engine 108 may generate the language processing model by anysuitable method, including without limitation a natural languageprocessing classification algorithm; language processing model mayinclude a natural language process classification model that enumeratesand/or derives statistical relationships between input term and outputterms. Algorithm to generate language processing model may include astochastic gradient descent algorithm, which may include a method thatiteratively optimizes an objective function, such as an objectivefunction representing a statistical estimation of relationships betweenterms, including relationships between input terms and output terms, inthe form of a sum of relationships to be estimated. In an alternative oradditional approach, sequential tokens may be modeled as chains, servingas the observations in a Hidden Markov Model (HMM). HMMs as used herein,are statistical models with inference algorithms that that may beapplied to the models. In such models, a hidden state to be estimatedmay include an association between an extracted word category ofphysiological data, a given relationship of such categories toprognostic labels, and/or a given category of prognostic labels. Theremay be a finite number of category of physiological data, a givenrelationship of such categories to prognostic labels, and/or a givencategory of prognostic labels to which an extracted word may pertain; anHMM inference algorithm, such as the forward-backward algorithm or theViterbi algorithm, may be used to estimate the most likely discretestate given a word or sequence of words. Language processing module 128may combine two or more approaches. For instance, and withoutlimitation, machine-learning program may use a combination ofNaive-Bayes (NB), Stochastic Gradient Descent (SGD), and parametergrid-searching classification techniques; the result may include aclassification algorithm that returns ranked associations.

Continuing to refer to FIG. 1, generating language processing model mayinclude generating a vector space, which may be a collection of vectors,defined as a set of mathematical objects that can be added togetherunder an operation of addition following properties of associativity,commutativity, existence of an identity element, and existence of aninverse element for each vector, and can be multiplied by scalar valuesunder an operation of scalar multiplication compatible with fieldmultiplication, and that has an identity element is distributive withrespect to vector addition, and is distributive with respect to fieldaddition. Each vector in an n-dimensional vector space may berepresented by an n-tuple of numerical values. Each unique extractedword and/or language element as described above may be represented by avector of the vector space. In an embodiment, each unique extractedand/or other language element may be represented by a dimension ofvector space; as a non-limiting example, each element of a vector mayinclude a number representing an enumeration of co-occurrences of theword and/or language element represented by the vector with another wordand/or language element. Vectors may be normalized, scaled according torelative frequencies of appearance and/or file sizes. In an embodimentassociating language elements to one another as described above mayinclude computing a degree of vector similarity between a vectorrepresenting each language element and a vector representing anotherlanguage element; vector similarity may be measured according to anynorm for proximity and/or similarity of two vectors, including withoutlimitation cosine similarity, which measures the similarity of twovectors by evaluating the cosine of the angle between the vectors, whichcan be computed using a dot product of the two vectors divided by thelengths of the two vectors. Degree of similarity may include any othergeometric measure of distance between vectors.

Still referring to FIG. 1, language processing module 128 may use acorpus of documents to generate associations between language elementsin a language processing module 128, and diagnostic engine 108 may thenuse such associations to analyze words extracted from one or moredocuments and determine that the one or more documents indicatesignificance of a category of physiological data, a given relationshipof such categories to prognostic labels, and/or a given category ofprognostic labels. In an embodiment, diagnostic engine 108 may performthis analysis using a selected set of significant documents, such asdocuments identified by one or more experts as representing goodscience, good clinical analysis, or the like; experts may identify orenter such documents via graphical user interface as described above inreference to FIG. 9, or may communicate identities of significantdocuments according to any other suitable method of electroniccommunication, or by providing such identity to other persons who mayenter such identifications into diagnostic engine 108. Documents may beentered into diagnostic engine 108 by being uploaded by an expert orother persons using, without limitation, file transfer protocol (FTP) orother suitable methods for transmission and/or upload of documents;alternatively or additionally, where a document is identified by acitation, a uniform resource identifier (URI), uniform resource locator(URL) or other datum permitting unambiguous identification of thedocument, diagnostic engine 108 may automatically obtain the documentusing such an identifier, for instance by submitting a request to adatabase or compendium of documents such as JSTOR as provided by IthakaHarbors, Inc. of New York

Continuing to refer to FIG. 1, whether an entry indicating significanceof a category of physiological data, a given relationship of suchcategories to prognostic labels, and/or a given category of prognosticlabels is entered via graphical user interface, alternative submissionmeans, and/or extracted from a document or body of documents asdescribed above, an entry or entries may be aggregated to indicate anoverall degree of significance. For instance, each category ofphysiological data, relationship of such categories to prognosticlabels, and/or category of prognostic labels may be given an overallsignificance score; overall significance score may, for instance, beincremented each time an expert submission and/or paper indicatessignificance as described above. Persons skilled in the art, uponreviewing the entirety of this disclosure will be aware of other ways inwhich scores may be generated using a plurality of entries, includingaveraging, weighted averaging, normalization, and the like. Significancescores may be ranked; that is, all categories of physiological data,relationships of such categories to prognostic labels, and/or categoriesof prognostic labels may be ranked according significance scores, forinstance by ranking categories of physiological data, relationships ofsuch categories to prognostic labels, and/or categories of prognosticlabels higher according to higher significance scores and loweraccording to lower significance scores. Categories of physiologicaldata, relationships of such categories to prognostic labels, and/orcategories of prognostic labels may be eliminated from current use ifthey fail a threshold comparison, which may include a comparison ofsignificance score to a threshold number, a requirement thatsignificance score belong to a given portion of ranking such as athreshold percentile, quartile, or number of top-ranked scores.Significance scores may be used to filter outputs as described infurther detail below; for instance, where a number of outputs aregenerated and automated selection of a smaller number of outputs isdesired, outputs corresponding to higher significance scores may beidentified as more probable and/or selected for presentation while otheroutputs corresponding to lower significance scores may be eliminated.Alternatively or additionally, significance scores may be calculated persample type; for instance, entries by experts, documents, and/ordescriptions of purposes of a given type of physiological test or samplecollection as described above may indicate that for that type ofphysiological test or sample collection a first category ofphysiological data, relationship of such category to prognostic labels,and/or category of prognostic labels is significant with regard to thattest, while a second category of physiological data, relationship ofsuch category to prognostic labels, and/or category of prognostic labelsis not significant; such indications may be used to perform asignificance score for each category of physiological data, relationshipof such category to prognostic labels, and/or category of prognosticlabels is or is not significant per type of physiological sample, whichthen may be subjected to ranking, comparison to thresholds and/orelimination as described above.

Still referring to FIG. 1, diagnostic engine 108 may detect furthersignificant categories of physiological data, relationships of suchcategories to prognostic labels, and/or categories of prognostic labelsusing machine-learning processes, including without limitationunsupervised machine-learning processes as described in further detailbelow; such newly identified categories, as well as categories enteredby experts in free-form fields as described above, may be added topre-populated lists of categories, lists used to identify languageelements for language learning module, and/or lists used to identifyand/or score categories detected in documents, as described above.

Continuing to refer to FIG. 1, in an embodiment, diagnostic engine 108may be configured, for instance as part of receiving the first trainingset 112, to associate at least correlated first prognostic label 120with at least a category from a list of significant categories ofprognostic labels. Significant categories of prognostic labels may beacquired, determined, and/or ranked as described above. As anon-limiting example, prognostic labels may be organized according torelevance to and/or association with a list of significant conditions. Alist of significant conditions may include, without limitation,conditions having generally acknowledged impact on longevity and/orquality of life; this may be determined, as a non-limiting example, by aproduct of relative frequency of a condition within the population withyears of life and/or years of able-bodied existence lost, on average, asa result of the condition. A list of conditions may be modified for agiven person to reflect a family history of the person; for instance, aperson with a significant family history of a particular condition orset of conditions, or a genetic profile having a similarly significantassociation therewith, may have a higher probability of developing suchconditions than a typical person from the general population, and as aresult diagnostic engine 108 may modify list of significant categoriesto reflect this difference.

Still referring to FIG. 1, diagnostic engine 108 is designed andconfigured to receive a second training set 132 including a plurality ofsecond data entries. Each second data entry of the second training set132 includes at least a second prognostic label 136; at least a secondprognostic label 136 may include any label suitable for use as at leasta first prognostic label 120 as described above. Each second data entryof the second training set 132 includes at least an ameliorative processlabel 140 correlated with the at least a second prognostic label 136,where correlation may include any correlation suitable for correlationof at least a first prognostic label 120 to at least an element ofphysiological data as described above. As used herein, an ameliorativeprocess label 140 is an identifier, which may include any form ofidentifier suitable for use as a prognostic label as described above,identifying a process that tends to improve a physical condition of auser, where a physical condition of a user may include, withoutlimitation, any physical condition identifiable using a prognosticlabel. Ameliorative processes may include, without limitation, exerciseprograms, including amount, intensity, and/or types of exerciserecommended. Ameliorative processes may include, without limitation,dietary or nutritional recommendations based on data includingnutritional content, digestibility, or the like. Ameliorative processesmay include one or more medical procedures. Ameliorative processes mayinclude one or more physical, psychological, or other therapies.Ameliorative processes may include one or more medications. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various processes that may be used as ameliorative processesconsistently with this disclosure.

Continuing to refer to FIG. 1, in an embodiment diagnostic engine 108may be configured, for instance as part of receiving second training set132, to associate the at least second prognostic label 136 with at leasta category from a list of significant categories of prognostic labels.This may be performed as described above for use of lists of significantcategories with regard to at least a first prognostic label 120.Significance may be determined, and/or association with at least acategory, may be performed for prognostic labels in first training set112 according to a first process as described above and for prognosticlabels in second training set 132 according to a second process asdescribed above.

Still referring to FIG. 1, diagnostic engine 108 may be configured, forinstance as part of receiving second training set 132, to associate atleast a correlated ameliorative process label 140 with at least acategory from a list of significant categories of ameliorative processlabels 136. In an embodiment, diagnostic engine 108 and/or a user deviceconnected to diagnostic engine 108 may provide a second graphical userinterface 144 which may include without limitation a form or othergraphical element having data entry fields, wherein one or more experts,including without limitation clinical and/or scientific experts, mayenter information describing one or more categories of prognostic labelsthat the experts consider to be significant as described above; fieldsin graphical user interface may provide options describing previouslyidentified categories, which may include a comprehensive ornear-comprehensive list of types of prognostic labels, for instance in“drop-down” lists, where experts may be able to select one or moreentries to indicate their usefulness and/or significance in the opinionof the experts. Fields may include free-form entry fields such astext-entry fields where an expert may be able to type or otherwise entertext, enabling expert to propose or suggest categories not currentlyrecorded. Graphical user interface or the like may include fieldscorresponding to ameliorative labels, where experts may enter datadescribing ameliorative labels and/or categories of ameliorative labelsthe experts consider related to entered categories of prognostic labels;for instance, such fields may include drop-down lists or otherpre-populated data entry fields listing currently recorded ameliorativelabels, and which may be comprehensive, permitting each expert to selectan ameliorative label and/or a plurality of ameliorative labels theexpert believes to be predicted and/or associated with each category ofprognostic labels selected by the expert. Fields for entry ofameliorative labels and/or categories of ameliorative labels may includefree-form data entry fields such as text entry fields; as describedabove, examiners may enter data not presented in pre-populated datafields in the free-form data entry fields. Alternatively oradditionally, fields for entry of ameliorative labels may enable anexpert to select and/or enter information describing or linked to acategory of ameliorative label that the expert considers significant,where significance may indicate likely impact on longevity, mortality,quality of life, or the like as described in further detail below.Graphical user interface may provide an expert with a field in which toindicate a reference to a document describing significant categories ofprognostic labels, relationships of such categories to ameliorativelabels, and/or significant categories of ameliorative labels. Suchinformation may alternatively be entered according to any other suitablemeans for entry of expert data as described above. Data concerningsignificant categories of prognostic labels, relationships of suchcategories to ameliorative labels, and/or significant categories ofameliorative labels may be entered using analysis of documents usinglanguage processing module 128 or the like as described above.

In an embodiment, and still referring to FIG. 1, diagnostic engine 108may extract at least a second data entry from one or more documents;extraction may be performed using any language processing method asdescribed above. Diagnostic engine 108 may be configured, for instanceas part of receiving second training set 132, to receive at least adocument describing at least a medical history and extract at least asecond data entry of plurality of second data entries from the at leasta document. A medical history document may include, for instance, adocument received from an expert and/or medical practitioner describingtreatment of a patient; document may be anonymized by removal of one ormore patient-identifying features from document. A medical historydocument may include a case study, such as a case study published in amedical journal or written up by an expert. A medical history documentmay contain data describing and/or described by a prognostic label; forinstance, the medical history document may list a diagnosis that amedical practitioner made concerning the patient, a finding that thepatient is at risk for a given condition and/or evinces some precursorstate for the condition, or the like. A medical history document maycontain data describing and/or described by an ameliorative processlabel 140; for instance, the medical history document may list atherapy, recommendation, or other ameliorative process that a medicalpractitioner described or recommended to a patient. A medical historydocument may describe an outcome; for instance, medical history documentmay describe an improvement in a condition describing or described by aprognostic label, and/or may describe that the condition did notimprove. Prognostic labels, ameliorative process labels 136, and/orefficacy of ameliorative process labels 136 may be extracted from and/ordetermined from one or more medical history documents using anyprocesses for language processing as described above; for instance,language processing module 128 may perform such processes. As anon-limiting example, positive and/or negative indications regardingameliorative processes identified in medical history documents may bedetermined in a manner described above for determination of positiveand/or negative indications regarding categories of physiological data,relationships of such categories to prognostic labels, and/or categoriesof prognostic labels.

With continued reference to FIG. 1, diagnostic engine 108 may beconfigured, for instance as part of receiving second training set 132,to receive at least a second data entry of the plurality of second dataentries from at least an expert. This may be performed, withoutlimitation using second graphical user interface as described above.

Referring again to FIG. 1, diagnostic engine 108 may be configured torecord at least a biological extraction. At least a biologicalextraction may include a physically extracted sample, which as usedherein, includes a sample obtained by removing and analyzing tissueand/or fluid. Physically extracted sample may include without limitationa blood sample, a tissue sample, a buccal swab, a mucous sample, a stoolsample, a hair sample, a fingernail sample, or the like. Physicallyextracted sample may include, as a non-limiting example, at least ablood sample. As a further non-limiting example, at least a biologicalextraction may include at least a genetic sample. At least a geneticsample may include a complete genome of a person or any portion thereof.At least a genetic sample may include a DNA sample and/or an RNA sample.At least a biological extraction may include an epigenetic sample, aproteomic sample, a tissue sample, a biopsy, and/or any other physicallyextracted sample. At least a biological extraction may include anendocrinal sample. As a further non-limiting example, the at least abiological extraction may include a signal from at least a sensorconfigured to detect physiological data of a user and recording the atleast a biological extraction as a function of the signal. At least asensor 148 may include any medical sensor and/or medical deviceconfigured to capture sensor data concerning a patient, including anyscanning, radiological and/or imaging device such as without limitationx-ray equipment, computer assisted tomography (CAT) scan equipment,positron emission tomography (PET) scan equipment, any form of magneticresonance imagery (MM) equipment, ultrasound equipment, optical scanningequipment such as photo-plethysmographic equipment, or the like. Atleast a sensor 148 may include any electromagnetic sensor, includingwithout limitation electroencephalographic sensors,magnetoencephalographic sensors, electrocardiographic sensors,electromyographic sensors, or the like. At least a sensor 148 mayinclude a temperature sensor. At least a sensor 148 may include anysensor that may be included in a mobile device and/or wearable device,including without limitation a motion sensor such as an inertialmeasurement unit (IMU), one or more accelerometers, one or moregyroscopes, one or more magnetometers, or the like. At least a wearableand/or mobile device sensor may capture step, gait, and/or othermobility data, as well as data describing activity levels and/orphysical fitness. At least a wearable and/or mobile device sensor maydetect heart rate or the like. At least a sensor 148 may detect anyhematological parameter including blood oxygen level, pulse rate, heartrate, pulse rhythm, blood sugar, and/or blood pressure. At least asensor 108 may be configured to detect internal and/or externalbiomarkers and/or readings. At least a sensor 148 may be a part ofsystem 100 or may be a separate device in communication with system 100.

Still referring to FIG. 1, at least a biological extraction may includeany data suitable for use as physiological state data as describedabove, including without limitation any result of any medical test,physiological assessment, cognitive assessment, psychologicalassessment, or the like. System 100 may receive at least a biologicalextraction from one or more other devices after performance; system 100may alternatively or additionally perform one or more assessments and/ortests to obtain at least a biological extraction, and/or one or moreportions thereof, on system 100. For instance, at least biologicalextraction may include or more entries by a user in a form or similargraphical user interface object; one or more entries may include,without limitation, user responses to questions on a psychological,behavioral, personality, or cognitive test. For instance, a computingdevice 104 may present to user a set of assessment questions designed orintended to evaluate a current state of mind of the user, a currentpsychological state of the user, a personality trait of the user, or thelike; a computing device 104 may provide user-entered responses to suchquestions directly as at least a biological extraction and/or mayperform one or more calculations or other algorithms to derive a scoreor other result of an assessment as specified by one or more testingprotocols, such as automated calculation of a Stanford-Binet and/orWechsler scale for IQ testing, a personality test scoring such as aMyers-Briggs test protocol, or other assessments that may occur topersons skilled in the art upon reviewing the entirety of thisdisclosure.

Alternatively or additionally, and with continued reference to FIG. 1,at least a biological extraction may include assessment and/orself-assessment data, and/or automated or other assessment results,obtained from a third-party device; third-party device may include,without limitation, a server or other device (not shown) that performsautomated cognitive, psychological, behavioral, personality, or otherassessments. Third-party device may include a device operated by aninformed advisor.

Still referring to FIG. 1, at least a biological extraction may includedata describing one or more test results, including results of mobilitytests, stress tests, dexterity tests, endocrinal tests, genetic tests,and/or electromyographic tests, biopsies, radiological tests, genetictests, and/or sensory tests. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various additionalexamples of at least a physiological sample consistent with thisdisclosure. At least a physiological sample may be added to biologicalextraction database 200.

With continued reference to FIG. 1, system 100 may include a prognosticlabel learner 152 operating on the diagnostic engine 108, the prognosticlabel learner 152 designed and configured to generate the at least aprognostic output as a function of the first training set 112 and the atleast a biological extraction. Prognostic label learner 152 may includeany hardware and/or software module. Prognostic label learner 152 isdesigned and configured to generate outputs using machine learningprocesses. A machine learning process is a process that automatedly usesa body of data known as “training data” and/or a “training set” togenerate an algorithm that will be performed by a computingdevice/module to produce outputs given data provided as inputs; this isin contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 1, prognostic label learner 152 may be designedand configured to generate at least a prognostic output by creating atleast a first machine-learning model 152 relating physiological statedata 116 to prognostic labels using the first training set 112 andgenerating the at least a prognostic output using the firstmachine-learning model 152; at least a first machine-learning model 152may include one or more models that determine a mathematicalrelationship between physiological state data 116 and prognostic labels.Such models may include without limitation model developed using linearregression models. Linear regression models may include ordinary leastsquares regression, which aims to minimize the square of the differencebetween predicted outcomes and actual outcomes according to anappropriate norm for measuring such a difference (e.g. a vector-spacedistance norm); coefficients of the resulting linear equation may bemodified to improve minimization. Linear regression models may includeridge regression methods, where the function to be minimized includesthe least-squares function plus term multiplying the square of eachcoefficient by a scalar amount to penalize large coefficients. Linearregression models may include least absolute shrinkage and selectionoperator (LASSO) models, in which ridge regression is combined withmultiplying the least-squares term by a factor of 1 divided by doublethe number of samples. Linear regression models may include a multi-tasklasso model wherein the norm applied in the least-squares term of thelasso model is the Frobenius norm amounting to the square root of thesum of squares of all terms. Linear regression models may include theelastic net model, a multi-task elastic net model, a least angleregression model, a LARS lasso model, an orthogonal matching pursuitmodel, a Bayesian regression model, a logistic regression model, astochastic gradient descent model, a perceptron model, a passiveaggressive algorithm, a robustness regression model, a Huber regressionmodel, or any other suitable model that may occur to persons skilled inthe art upon reviewing the entirety of this disclosure. Linearregression models may be generalized in an embodiment to polynomialregression models, whereby a polynomial equation (e.g. a quadratic,cubic or higher-order equation) providing a best predicted output/actualoutput fit is sought; similar methods to those described above may beapplied to minimize error functions, as will be apparent to personsskilled in the art upon reviewing the entirety of this disclosure.

With continued reference to FIG. 1, machine-learning algorithms maygenerate prognostic output as a function of a classification of at leasta prognostic label. Classification as used herein includes pairing orgrouping prognostic labels as a function of a shared commonality.Classification may include for example, groupings, pairings, and/ortrends between physiological data and current prognostic label, futureprognostic label, and the like. In an embodiment, machine-learningalgorithms may examine relationships between a future propensity of auser to develop a condition based on current user physiological data.Machine-learning algorithms may include any and all algorithms asperformed by any modules, described herein for prognostic label learner152. For example, machine-learning algorithms may relate fasting bloodglucose readings of a user to user's future propensity to developdiabetes. Machine-learning algorithms may examine precursor conditionand future propensity to develop a subsequent disorder. For example,machine-learning algorithms may examine a user diagnosed with chickenpox and user's future propensity to subsequently develop shingles. Inyet another non-limiting example, machine-learning algorithms mayexamine infection with human papillomavirus (HPV) and subsequent cancerdiagnosis. Machine-learning algorithms may examine a user's propensityto have recurring attacks of a disease or condition, for example a userwith elevated uric acid levels and repeated attacks of gout.Machine-learning algorithms may examine user's genetic predisposition todevelop a certain condition or disease. For example, machine-learningalgorithms may examine presence of hereditary non-polyposis colorectalcancer (HNPCC) commonly known as lynch syndrome, and subsequentdiagnosis of colorectal cancer. In yet another non-limiting example,machine-learning algorithms may examine presence of abnormal squamouscells and/or abnormal glandular cells in the cervix and subsequentdevelopment of cervical cancer. Machine-learning algorithms may examineprogression of disease state, for example progression of humanimmunodeficiency virus (HIV) is marked by decline of CD4+T-Cells, with acount below 200 leading to a diagnosis of acquired immunodeficiencysyndrome (AIDS). In yet another non-limiting example, progression ofdiabetes may be marked by increases of hemoglobin A1C levels with alevel of 6.5% indicating a diagnosis of diabetes. Machine-learningalgorithms may examine progression of disease by certain age groups. Forexample, progression of Multiple Sclerosis in users between the age of20-30 as compared to progression of Multiple Sclerosis in users betweenthe age of 70-80. Machine-learning algorithms may be examiningprogression of aging such as measurements of telomere length and/oroxidative stress levels and chance mortality risk. Machine-learningalgorithms may examine development of co-morbid conditions when adisease or conditions is already present. For example, machine-learningalgorithms may examine a user diagnosed with depression and subsequentdiagnosis of a co-morbid condition such as migraines, generalizedanxiety disorder, antisocial personality disorder, agoraphobia,obsessive-compulsive disorder, drug dependence alcohol dependence,and/or panic disorder. Machine-learning algorithms may examine a user'slifetime chance of developing a certain disease or condition, such as auser's lifetime risk of heart disease, Alzheimer's disease, diabetes andthe like. Machine-learning algorithms may be grouped and implementedaccording to any of the methodologies as described in this disclosure.

Continuing to refer to FIG. 1, machine-learning algorithm used togenerate first machine-learning model 152 may include, withoutlimitation, linear discriminant analysis. Machine-learning algorithm mayinclude quadratic discriminate analysis. Machine-learning algorithms mayinclude kernel ridge regression. Machine-learning algorithms may includesupport vector machines, including without limitation support vectorclassification-based regression processes. Machine-learning algorithmsmay include stochastic gradient descent algorithms, includingclassification and regression algorithms based on stochastic gradientdescent. Machine-learning algorithms may include nearest neighbors'algorithms. Machine-learning algorithms may include Gaussian processessuch as Gaussian Process Regression. Machine-learning algorithms mayinclude cross-decomposition algorithms, including partial least squaresand/or canonical correlation analysis. Machine-learning algorithms mayinclude naïve Bayes methods. Machine-learning algorithms may includealgorithms based on decision trees, such as decision tree classificationor regression algorithms. Machine-learning algorithms may includeensemble methods such as bagging meta-estimator, forest of randomizedtress, AdaBoost, gradient tree boosting, and/or voting classifiermethods. Machine-learning algorithms may include neural net algorithms,including convolutional neural net processes.

Still referring to FIG. 1, prognostic label learner 152 may generateprognostic output using alternatively or additional artificialintelligence methods, including without limitation by creating anartificial neural network, such as a convolutional neural networkcomprising an input layer of nodes, one or more intermediate layers, andan output layer of nodes. Connections between nodes may be created viathe process of “training” the network, in which elements from a trainingdataset are applied to the input nodes, a suitable training algorithm(such as Levenberg-Marquardt, conjugate gradient, simulated annealing,or other algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning. This network may be trained using first trainingset 112; the trained network may then be used to apply detectedrelationships between elements of physiological state data 116 andprognostic labels. Referring again to FIG. 1, machine-learningalgorithms may include unsupervised processes; unsupervised processesmay, as a non-limiting example, be executed by an unsupervised learningmodule 704 executing on diagnostic engine 108 and/or on anothercomputing device in communication with diagnostic engine 108, which mayinclude any hardware or software module. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data. Forinstance, and without limitation, prognostic label learner 152 and/ordiagnostic engine 108 may perform an unsupervised machine learningprocess on first training set 112, which may cluster data of firsttraining set 112 according to detected relationships between elements ofthe first training set 112, including without limitation correlations ofelements of physiological state data 116 to each other and correlationsof prognostic labels to each other; such relations may then be combinedwith supervised machine learning results to add new criteria forprognostic label learner 152 to apply in relating physiological statedata 116 to prognostic labels. As a non-limiting, illustrative example,an unsupervised process may determine that a first element ofphysiological data acquired in a blood test correlates closely with asecond element of physiological data, where the first element has beenlinked via supervised learning processes to a given prognostic label,but the second has not; for instance, the second element may not havebeen defined as an input for the supervised learning process, or maypertain to a domain outside of a domain limitation for the supervisedlearning process. Continuing the example a close correlation betweenfirst element of physiological state data 116 and second element ofphysiological state data 116 may indicate that the second element isalso a good predictor for the prognostic label; second element may beincluded in a new supervised process to derive a relationship or may beused as a synonym or proxy for the first physiological element byprognostic label learner 152.

Still referring to FIG. 1, diagnostic engine 108 and/or prognostic labellearner 152 may detect further significant categories of physiologicaldata, relationships of such categories to prognostic labels, and/orcategories of prognostic labels using machine-learning processes,including without limitation unsupervised machine-learning processes asdescribed above; such newly identified categories, as well as categoriesentered by experts in free-form fields as described above, may be addedto pre-populated lists of categories, lists used to identify languageelements for language learning module, and/or lists used to identifyand/or score categories detected in documents, as described above. In anembodiment, as additional data is added to system 100, prognostic labellearner 152 and/or diagnostic engine 108 may continuously or iterativelyperform unsupervised machine-learning processes to detect relationshipsbetween different elements of the added and/or overall data; in anembodiment, this may enable system 100 to use detected relationships todiscover new correlations between known biomarkers, prognostic labels,and/or ameliorative labels and one or more elements of data in largebodies of data, such as genomic, proteomic, and/or microbiome-relateddata, enabling future supervised learning and/or lazy learning processesas described in further detail below to identify relationships between,e.g., particular clusters of genetic alleles and particular prognosticlabels and/or suitable ameliorative labels. Use of unsupervised learningmay greatly enhance the accuracy and detail with which system may detectprognostic labels and/or ameliorative labels.

With continued reference to FIG. 1, unsupervised processes may besubjected to domain limitations. For instance, and without limitation,an unsupervised process may be performed regarding a comprehensive setof data regarding one person, such as a comprehensive medical history,set of test results, and/or physiological data such as genomic,proteomic, and/or other data concerning that persons. As anothernon-limiting example, an unsupervised process may be performed on dataconcerning a particular cohort of persons; cohort may include, withoutlimitation, a demographic group such as a group of people having ashared age range, ethnic background, nationality, sex, and/or gender.Cohort may include, without limitation, a group of people having ashared value for an element and/or category of physiological data, agroup of people having a shared value for an element and/or category ofprognostic label, and/or a group of people having a shared value and/orcategory of ameliorative label; as illustrative examples, cohort couldinclude all people having a certain level or range of levels of bloodtriglycerides, all people diagnosed with type II diabetes, all peoplewho regularly run between 10 and 15 miles per week, or the like. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of a multiplicity of ways in which cohorts and/or other sets ofdata may be defined and/or limited for a particular unsupervisedlearning process.

Still referring to FIG. 1, prognostic label learner 152 mayalternatively or additionally be designed and configured to generate atleast a prognostic output by executing a lazy learning process as afunction of the first training set 112 and the at least a biologicalextraction; lazy learning processes may be performed by a lazy learningmodule 708 executing on diagnostic engine 108 and/or on anothercomputing device in communication with diagnostic engine 108, which mayinclude any hardware or software module. A lazy-learning process and/orprotocol, which may alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover a “first guess” ata prognostic label associated with biological extraction, using firsttraining set 112. As a non-limiting example, an initial heuristic mayinclude a ranking of prognostic labels according to relation to a testtype of at least a biological extraction, one or more categories ofphysiological data identified in test type of at least a biologicalextraction, and/or one or more values detected in at least a biologicalextraction; ranking may include, without limitation, ranking accordingto significance scores of associations between elements of physiologicaldata and prognostic labels, for instance as calculated as describedabove. Heuristic may include selecting some number of highest-rankingassociations and/or prognostic labels. Prognostic label learner 152 mayalternatively or additionally implement any suitable “lazy learning”algorithm, including without limitation a K-nearest neighbors algorithm,a lazy naïve Bayes algorithm, or the like; persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variouslazy-learning algorithms that may be applied to generate prognosticoutputs as described in this disclosure, including without limitationlazy learning applications of machine-learning algorithms as describedin further detail below.

In an embodiment, and continuing to refer to FIG. 1, prognostic labellearner 152 may generate a plurality of prognostic labels havingdifferent implications for a particular person. For instance, where theat least a physiological sample includes a result of a dexterity test, alow score may be consistent with amyotrophic lateral sclerosis,Parkinson's disease, multiple sclerosis, and/or any number of less severdisorders or tendencies associated with lower levels of dexterity. Insuch a situation, prognostic label learner 152 and/or diagnostic engine108 may perform additional processes to resolve ambiguity. Processes mayinclude presenting multiple possible results to a medical practitioner,informing the medical practitioner that one or more follow-up testsand/or physiological samples are needed to further determine a moredefinite prognostic label. Alternatively or additionally, processes mayinclude additional machine learning steps; for instance, where referenceto a model generated using supervised learning on a limited domain hasproduced multiple mutually exclusive results and/or multiple resultsthat are unlikely all to be correct, or multiple different supervisedmachine learning models in different domains may have identifiedmutually exclusive results and/or multiple results that are unlikely allto be correct. In such a situation, prognostic label learner 152 and/ordiagnostic engine 108 may operate a further algorithm to determine whichof the multiple outputs is most likely to be correct; algorithm mayinclude use of an additional supervised and/or unsupervised model.Alternatively or additionally, prognostic label learner 152 may performone or more lazy learning processes using a more comprehensive set ofuser data to identify a more probably correct result of the multipleresults. Results may be presented and/or retained with rankings, forinstance to advise a medical professional of the relative probabilitiesof various prognostic labels being correct; alternatively oradditionally, prognostic labels associated with a probability ofcorrectness below a given threshold and/or prognostic labelscontradicting results of the additional process, may be eliminated. As anon-limiting example, an endocrinal test may determine that a givenperson has high levels of dopamine, indicating that a poor pegboardperformance is almost certainly not being caused by Parkinson's disease,which may lead to Parkinson's being eliminated from a list of prognosticlabels associated with poor pegboard performance, for that person.Similarly, a genetic test may eliminate Huntington's disease, or anotherdisease definitively linked to a given genetic profile, as a cause.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various ways in which additional processingmay be used to determine relative likelihoods of prognostic labels on alist of multiple prognostic labels, and/or to eliminate some labels fromsuch a list. Prognostic output 712 may be provided to user output deviceas described in further detail below.

Still referring to FIG. 1, diagnostic engine 108 includes anameliorative process label learner 160 operating on the diagnosticengine 108, the ameliorative process label learner 160 designed andconfigured to generate the at least an ameliorative output as a functionof the second training set 132 and the at least a prognostic output.Ameliorative process label learner 160 may include any hardware orsoftware module suitable for use as a prognostic label learner 152 asdescribed above. Ameliorative process label learner 160 is amachine-learning module as described above; ameliorative process labellearner 160 may perform any machine-learning process or combination ofprocesses suitable for use by a prognostic label learner 152 asdescribed above. For instance, and without limitation, and ameliorativeprocess label learner 160 may be configured to create a secondmachine-learning model 160 relating prognostic labels to ameliorativelabels using the second training set 132 and generate the at least anameliorative output using the second machine-learning model 160; secondmachine-learning model 160 may be generated according to any process,process steps, or combination of processes and/or process steps suitablefor creation of first machine learning model. In an embodiment,ameliorative process label learner 160 may use data from first trainingset 112 as well as data from second training set 132; for instance,ameliorative process label learner 160 may use lazy learning and/ormodel generation to determine relationships between elements ofphysiological data, in combination with or instead of prognostic labels,and ameliorative labels. Where ameliorative process label learner 160determines relationships between elements of physiological data andameliorative labels directly, this may determine relationships betweenprognostic labels and ameliorative labels as well owing to the existenceof relationships determined by prognostic label learner 152.

Referring again to FIG. 1, system 100 may include a plan generationmodule 168 operating on the a computing device 104. Plan generatormodule 168 may include any suitable hardware or hardware module. In anembodiment, plan generator module 168 is designed and configured togenerate a comprehensive instruction set 172 associated with the userbased on the diagnostic output. In an embodiment, comprehensiveinstruction set 172 is a data structure containing instructions to beprovided to the user to explain the user's current prognostic status, asreflected by one or more prognostic outputs and provide the user with aplan based on the at least an ameliorative output, to achieve that. Inan embodiment, comprehensive instruction set 172 may be generated basedon at least an informed advisor output. Comprehensive instruction set172 may include but is not limited to a program, strategy, summary,recommendation, or any other type of interactive platform that may beconfigured to comprise information associated with the user, anapplicable verified external source, and one or more outputs derivedfrom the analyses performed on the extraction from the user.Comprehensive instruction set 172 may describe to a user a futureprognostic status to aspire to. In an embodiment, plan generation module168 is configured to receive at least an element of user data and filterthe diagnostic output using the at least an element of user data.

Referring again to FIG. 1, system 100 may include a client-interfacemodule 176. Client-interface module 176 may include any suitablehardware or software module. Client-interface module 176 may designedand configured to transmit comprehensive instruction set 172 to at leasta user client device 180 associated with the user. A user client device180 may include, without limitation, a display in communication withdiagnostic engine 108; display may include any display as described inthis disclosure. A user client device 180 may include an additioncomputing device, such as a mobile device, laptop, desktop computer, orthe like; as a non-limiting example, the user client device 180 may be acomputer and/or workstation operated by a medical professional. Outputmay be displayed on at least a user client device 180 using an outputgraphical user interface; output graphical user interface may display atleast a current prognostic descriptor 900, at least a future prognosticdescriptor 924, and/or at least an ameliorative process descriptor 928.

With continued reference to FIG. 1, system 100 includes at least anadvisory module executing on the a computing device 104. At least anadvisory module 184 may include any suitable hardware or softwaremodule. In an embodiment, at least an advisory module 184 is designedand configured to generate at least an advisory output as a function ofthe comprehensive instruction set 172 and may transmit the advisoryoutput to at least an advisor client device 188. At least an advisorclient device 188 may include any device suitable for use as a userclient device 180 as described above. At least an advisor client device188 may operate on system 100 and may be a user client device 180 asdescribed above; that is, at least an advisory output may be output tothe user client device 180. Alternatively or additionally, at least anadvisor client device 188 may be operated by an informed advisor,defined for the purposes of this disclosure as any person besides theuser who has access to information useable to aid user in interactionwith artificial intelligence advisory system. An informed advisor mayinclude, without limitation, a medical professional such as a doctor,nurse, nurse practitioner, functional medicine practitioner, anyprofessional with a career in medicine, nutrition, genetics, fitness,life sciences, insurance, and/or any other applicable industry that maycontribute information and data to system 100 regarding medical needs.An informed advisor may include a spiritual or philosophical advisor,such as a religious leader, pastor, imam, rabbi, or the like. Aninformed advisor may include a physical fitness advisor, such as withoutlimitation a personal trainer, instructor in yoga or martial arts,sports coach, or the like.

With continued reference to FIG. 1, advisory module 184 is configured todetect a nutritional advisory intervention event as a function of adiagnostic output, generate a response to the advisory interventionevent wherein the response identifies an advisory action, and transmitthe response to a user client device. A “nutritional advisoryintervention event,” as used in this disclosure, is any incidentperformed by a user, that requires a nutritional consultation. Anyincident, may include any question, episode, event, diet slip, foodselection, food habit, food choice, supplement choice, supplementquestion, and/or supplement selection that requires input and/orcommunication from an informed advisor. A nutritional consultation, mayinclude any dialogue, and/or communication regarding a nutritionaladvisory intervention event. For instance and without limitation, anutritional advisory intervention event may include an incident where auser has a question about a meal a user can purchase at a grocery store.In yet another non-limiting example, a nutritional advisory interventionevent may include an episode where a user has been following a certaineating pattern to aid in treatment of an autoimmune condition, and theuser has slipped and once again started to eat pro-inflammatory foodswhich do not aid in the treatment of the user's autoimmune condition.Advisory module may detect a nutritional advisory intervention event asa function of a diagnostic output. For example, a diagnostic output suchas hypothyroidism may cause advisory module to detect a nutritionaladvisory intervention event that indicates initiation of an autoimmuneprotocol diet. In yet another non-limiting example, a diagnostic outputsuch as type 2 diabetes mellitus may cause advisory module to detect anutritional advisory intervention event to ensure selection andconsumption of foods low in carbohydrates and refined sugars.

With continued reference to FIG. 1, advisory module may detect anutritional advisory intervention based on one or more inputs from auser client device. For example, a user may self-report a nutritionaladvisory intervention event, such as when a user may be shopping forgroceries and may be distraught about what groceries the user shouldpurchase that will comply with a low-FODMAP diet. In yet anothernon-limiting example, a user may self-report a nutritional advisoryintervention event, such as when a user may self-report that a user hasnot complied with a vegan diet and the user would like to reset andreinitiate a vegan diet. In yet another non-limiting example, a user mayself-report a nutritional advisory intervention event when the user hasa question about what menu item would be safe for the user to order thatwill not aggravate the user's symptoms of irritable bowel syndrome(IBS).

With continued reference to FIG. 1, advisory module may detect anutritional advisory intervention event based on input generated from auser client device operated by a family member, friend, spouse,ex-boyfriend, ex-boyfriend, co-worker, acquaintance, and/or any otherhuman being who may be concerned about the user. For example, a friendwho may exercise with the user may self-report that the user has notbeen showing up to exercise class, and that the user has not beencompliant with the user's recommended meal plan to follow a ketogenicdiet. In yet another non-limiting example, an ex-boyfriend may generatea nutritional advisory intervention event that reports the user has beeneating lots of fatty deep friend foods, when the user has highcholesterol and high triglycerides and is supposed to be eating onlysteamed fish and vegetables. Advisory module may detect a nutritionaladvisory intervention event utilizing a biological extraction. Advisorymodule may retrieve one or more biological extractions pertaining to auser from biological extraction database. Advisory module may compareone or more biological extractions to a reference range, to determine ifa biological extraction falls within normal limits contained within thereference range, or if one or more biological extractions are not withinnormal limits contained within the reference range and may requireinterpretation by an informed advisor. For instance and withoutlimitation, an elevated fasting blood glucose level outside of normallimits may trigger a nutritional advisory intervention event. In yetanother non-limiting example, a low thyroid stimulating hormone (TSH)level may trigger a nutritional advisory intervention event. In yetanother non-limiting example, a stool sample containing an alteredmicrobiome may trigger a nutritional advisory intervention event.

With continued reference to FIG. 1, advisory module is configured togenerate a response to an advisory intervention event wherein theresponse identifies an advisory action. An “advisory action,” as used inthis disclosure, is any response generated in response to a nutritionaladvisory intervention event. An advisory action may include anadjustment to a comprehensive instruction set. For example, an advisoryaction may remove one or more additional foods that a user should notconsume. In yet another non-limiting example, an advisory action mayrecommend one or more additional foods that a user should start toconsume. An advisory action may be based on one or more inputs containedwithin expert knowledge database. For example, a nutritional advisoryintervention event may indicate that a user has not been compliant witha low carbohydrate diet, because the user does not like the lowcarbohydrate options user can eat for breakfast. In such an instance,advisory module may generate an advisory action, that adjusts user'scomprehensive instruction set to allow the user to consume highercarbohydrate foods such as oatmeal but only at breakfast. Advisorymodule may generate an advisory action to include a textual output. A“textual output,” as used in this disclosure, is any textual, character,and/or numerical text generated in response to an advisory interventionevent. A textual output may include one or more directions and/orinstruction in response to a question posed by a user. A textual outputmay include one or more words of encouragement, support, and/orreassurance for a user. For example, a textual output may encourage auser to stick with a low carbohydrate diet or continue to support auser's weight loss efforts through diet. A textual output may encouragea user not to engage in certain behaviors, such as a reminder to a userwith high blood sugar to not eat a donut or to only eat minimal amountsof fruit each day.

With continued reference to FIG. 1, advisory module may be configured togenerate a response that contains an urgency label. An “urgency label,”as used in this disclosure, is an element of data describing anemergency situation that requires immediate attention. An urgency labelmay be generated in response to a crisis situation, a situation where auser may require immediate consultation with an informed advisor and thelike. Advisory module may locate an emergency nutritional informedadvisor. An emergency nutritional informed advisor may include anyinformed advisor who may be on call and who may be trained to respond toan emergency situation. Advisory module may transmit a responsecontaining an urgency label to an advisor client device operated by theemergency nutritional informed advisor. In such an instance, theemergency nutritional informed advisor may then get in touch with theuser and provide consultation as needed. Advisory module may transmitthe response to an advisor client device utilizing any networkmethodology as described herein.

Referring now to FIG. 2, data incorporated in first training set 112and/or second training set 132 may be incorporated in one or moredatabases. As a non-limiting example, one or elements of physiologicalstate data may be stored in and/or retrieved from a biologicalextraction database 200. A biological extraction database 200 mayinclude any data structure for ordered storage and retrieval of data,which may be implemented as a hardware or software module. A biologicalextraction database 200 may be implemented, without limitation, as arelational database, a key-value retrieval datastore such as a NOSQLdatabase, or any other format or structure for use as a datastore that aperson skilled in the art would recognize as suitable upon review of theentirety of this disclosure. A biological extraction database 200 mayinclude a plurality of data entries and/or records corresponding toelements of physiological data as described above. Data entries and/orrecords may describe, without limitation, data concerning particularphysiological samples that have been collected; entries may describereasons for collection of samples, such as without limitation one ormore conditions being tested for, which may be listed with relatedprognostic labels. Data entries may include prognostic labels and/orother descriptive entries describing results of evaluation of pastphysiological samples, including diagnoses that were associated withsuch samples, prognoses and/or conclusions regarding likelihood offuture diagnoses that were associated with such samples, and/or othermedical or diagnostic conclusions that were derived. Such conclusionsmay have been generated by system 100 in previous iterations of methods,with or without validation of correctness by medical professionals. Dataentries in a biological extraction database 200 may be flagged with orlinked to one or more additional elements of information, which may bereflected in data entry cells and/or in linked tables such as tablesrelated by one or more indices in a relational database; one or moreadditional elements of information may include data associating aphysiological sample and/or a person from whom a physiological samplewas extracted or received with one or more cohorts, includingdemographic groupings such as ethnicity, sex, age, income, geographicalregion, or the like, one or more common diagnoses or physiologicalattributes shared with other persons having physiological samplesreflected in other data entries, or the like. Additional elements ofinformation may include one or more categories of physiological data asdescribed above. Additional elements of information may includedescriptions of particular methods used to obtain physiological samples,such as without limitation physical extraction of blood samples or thelike, capture of data with one or more sensors, and/or any otherinformation concerning provenance and/or history of data acquisition.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various ways in which data entries in abiological extraction database 200 may reflect categories, cohorts,and/or populations of data consistently with this disclosure.

Referring now to FIG. 3, one or more database tables in biologicalextraction database 200 may include, as a non-limiting example, aprognostic link table 300. Prognostic link table 300 may be a tablerelating physiological sample data as described above to prognosticlabels; for instance, where an expert has entered data relating aprognostic label to a category of physiological sample data and/or to anelement of physiological sample data via first graphical user interface124 as described above, one or more rows recording such an entry may beinserted in prognostic link table 300. Alternatively or additionally,linking of prognostic labels to physiological sample data may beperformed entirely in a prognostic label database as described below.

With continued reference to FIG. 3, biological extraction database 200may include tables listing one or more samples according to samplesource. For instance, and without limitation, biological extractiondatabase 200 may include a fluid sample table 304 listing samplesacquired from a person by extraction of fluids, such as withoutlimitation blood, lymph cerebrospinal fluid, or the like. As anothernon-limiting example, biological extraction database 200 may include asensor data table 308, which may list samples acquired using one or moresensors, for instance as described in further detail below. As a furthernon-limiting example, biological extraction database 200 may include agenetic sample table 312, which may list partial or entire sequences ofgenetic material. Genetic material may be extracted and amplified, as anon-limiting example, using polymerase chain reactions (PCR) or thelike. As a further example, also non-limiting, biological extractiondatabase 200 may include a medical report table 316, which may listtextual descriptions of medical tests, including without limitationradiological tests or tests of strength and/or dexterity or the like.Data in medical report table may be sorted and/or categorized using alanguage processing module 312, for instance, translating a textualdescription into a numerical value and a label corresponding to acategory of physiological data; this may be performed using any languageprocessing algorithm or algorithms as referred to in this disclosure. Asanother non-limiting example, biological extraction database 200 mayinclude a tissue sample table 320, which may record physiologicalsamples obtained using tissue samples. Tables presented above arepresented for exemplary purposes only; persons skilled in the art willbe aware of various ways in which data may be organized in biologicalextraction database 200 consistently with this disclosure.

Referring again to FIG. 2, diagnostic engine 108 and/or another devicein system 100 may populate one or more fields in biological extractiondatabase 200 using expert information, which may be extracted orretrieved from an expert knowledge database 204. An expert knowledgedatabase 204 may include any data structure and/or data store suitablefor use as a biological extraction database 200 as described above.Expert knowledge database 204 may include data entries reflecting one ormore expert submissions of data such as may have been submittedaccording to any process described above in reference to FIG. 1,including without limitation by using first graphical user interface 124and/or second graphical user interface 140. Expert knowledge databasemay include one or more fields generated by language processing module128, such as without limitation fields extracted from one or moredocuments as described above. For instance, and without limitation, oneor more categories of physiological data and/or related prognosticlabels and/or categories of prognostic labels associated with an elementof physiological state data as described above may be stored ingeneralized from in an expert knowledge database 204 and linked to,entered in, or associated with entries in a biological extractiondatabase 200. Documents may be stored and/or retrieved by diagnosticengine 108 and/or language processing module 128 in and/or from adocument database 208; document database 208 may include any datastructure and/or data store suitable for use as biological extractiondatabase 200 as described above. Documents in document database 208 maybe linked to and/or retrieved using document identifiers such as URIand/or URL data, citation data, or the like; persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variousways in which documents may be indexed and retrieved according tocitation, subject matter, author, date, or the like as consistent withthis disclosure.

Referring now to FIG. 4, an exemplary embodiment of an expert knowledgedatabase 204 is illustrated. Expert knowledge database 204 may, as anon-limiting example, organize data stored in the expert knowledgedatabase 204 according to one or more database tables. One or moredatabase tables may be linked to one another by, for instance, commoncolumn values. For instance, a common column between two tables ofexpert knowledge database 200 may include an identifier of an expertsubmission, such as a form entry, textual submission, expert paper, orthe like, for instance as defined below; as a result, a query may beable to retrieve all rows from any table pertaining to a givensubmission or set thereof. Other columns may include any other categoryusable for organization or subdivision of expert data, including typesof expert data, names and/or identifiers of experts submitting the data,times of submission, or the like; persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which expert data from one or more tables may be linked and/orrelated to expert data in one or more other tables.

Still referring to FIG. 4, one or more database tables in expertknowledge database 204 may include, as a non-limiting example, an expertprognostic table 400. Expert prognostic table 400 may be a tablerelating physiological sample data as described above to prognosticlabels; for instance, where an expert has entered data relating aprognostic label to a category of physiological sample data and/or to anelement of physiological sample data via first graphical user interface120 as described above, one or more rows recording such an entry may beinserted in expert prognostic table 400. In an embodiment, a formsprocessing module 404 may sort data entered in a submission via firstgraphical user interface 120 by, for instance, sorting data from entriesin the first graphical user interface 120 to related categories of data;for instance, data entered in an entry relating in the first graphicaluser interface 120 to a prognostic label may be sorted into variablesand/or data structures for storage of prognostic labels, while dataentered in an entry relating to a category of physiological data and/oran element thereof may be sorted into variables and/or data structuresfor the storage of, respectively, categories of physiological data orelements of physiological data. Where data is chosen by an expert frompre-selected entries such as drop-down lists, data may be storeddirectly; where data is entered in textual form, language processingmodule 128 may be used to map data to an appropriate existing label, forinstance using a vector similarity test or other synonym-sensitivelanguage processing test to map physiological data to an existing label.Alternatively or additionally, when a language processing algorithm,such as vector similarity comparison, indicates that an entry is not asynonym of an existing label, language processing module may indicatethat entry should be treated as relating to a new label; this may bedetermined by, e.g., comparison to a threshold number of cosinesimilarity and/or other geometric measures of vector similarity of theentered text to a nearest existent label, and determination that adegree of similarity falls below the threshold number and/or a degree ofdissimilarity falls above the threshold number. Data from expert textualsubmissions 408, such as accomplished by filling out a paper or PDF formand/or submitting narrative information, may likewise be processed usinglanguage processing module 128. Data may be extracted from expert papers412, which may include without limitation publications in medical and/orscientific journals, by language processing module 128 via any suitableprocess as described herein. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various additionalmethods whereby novel terms may be separated from already-classifiedterms and/or synonyms therefore, as consistent with this disclosure.Expert prognostic table 400 may include a single table and/or aplurality of tables; plurality of tables may include tables forparticular categories of prognostic labels such as a current diagnosistable, a future prognosis table, a genetic tendency table, a metabolictendency table, and/or an endocrinal tendency table (not shown), to namea few non-limiting examples presented for illustrative purposes only.

With continued reference to FIG. 4, one or more database tables inexpert knowledge database 204 may include, as a further non-limitingexample tables listing one or more ameliorative process labels; expertdata populating such tables may be provided, without limitation, usingany process described above, including entry of data from secondgraphical user interface 140 via forms processing module 404 and/orlanguage processing module 128, processing of textual submissions 408,or processing of expert papers 412. For instance, and withoutlimitation, an ameliorative nutrition table 416 may list one or moreameliorative processes based on nutritional instructions, and/or linksof such one or more ameliorative processes to prognostic labels, asprovided by experts according to any method of processing and/orentering expert data as described above. As a further example anameliorative action table 420 may list one or more ameliorativeprocesses based on instructions for actions a user should take,including without limitation exercise, meditation, and/or cessation ofharmful eating, substance abuse, or other habits, and/or links of suchone or more ameliorative processes to prognostic labels, as provided byexperts according to any method of processing and/or entering expertdata as described above. As an additional example, an ameliorativesupplement table 424 may list one or more ameliorative processes basedon nutritional supplements, such as vitamin pills or the like, and/orlinks of such one or more ameliorative processes to prognostic labels,as provided by experts according to any method of processing and/orentering expert data as described above. As a further non-limitingexample, an ameliorative medication table 428 may list one or moreameliorative processes based on medications, including withoutlimitation over-the-counter and prescription pharmaceutical drugs,and/or links of such one or more ameliorative processes to prognosticlabels, as provided by experts according to any method of processingand/or entering expert data as described above. As an additionalexample, a counterindication table 432 may list one or morecounter-indications for one or more ameliorative processes;counterindications may include, without limitation allergies to one ormore foods, medications, and/or supplements, side-effects of one or moremedications and/or supplements, interactions between medications, foods,and/or supplements, exercises that should not be used given one or moremedical conditions, injuries, disabilities, and/or demographiccategories, or the like. Tables presented above are presented forexemplary purposes only; persons skilled in the art will be aware ofvarious ways in which data may be organized in expert knowledge database204 consistently with this disclosure.

Referring again to FIG. 2, a prognostic label database 212, which may beimplemented in any manner suitable for implementation of biologicalextraction database 200, may be used to store prognostic labels used insystem 100, including any prognostic labels correlated with elements ofphysiological data in first training set 112 as described above;prognostic labels may be linked to or refer to entries in biologicalextraction database 200 to which prognostic labels correspond. Linkingmay be performed by reference to historical data concerningphysiological samples, such as diagnoses, prognoses, and/or othermedical conclusions derived from physiological samples in the past;alternatively or additionally, a relationship between a prognostic labeland a data entry in biological extraction database 200 may be determinedby reference to a record in an expert knowledge database 204 linking agiven prognostic label to a given category of physiological sample asdescribed above. Entries in prognostic label database 212 may beassociated with one or more categories of prognostic labels as describedabove, for instance using data stored in and/or extracted from an expertknowledge database 204.

Referring now to FIG. 5, an exemplary embodiment of a prognostic labeldatabase 212 is illustrated. Prognostic label database 212 may, as anon-limiting example, organize data stored in the prognostic labeldatabase 212 according to one or more database tables. One or moredatabase tables may be linked to one another by, for instance, commoncolumn values. For instance, a common column between two tables ofprognostic label database 212 may include an identifier of an expertsubmission, such as a form entry, textual submission, expert paper, orthe like, for instance as defined below; as a result, a query may beable to retrieve all rows from any table pertaining to a givensubmission or set thereof. Other columns may include any other categoryusable for organization or subdivision of expert data, including typesof expert data, names and/or identifiers of experts submitting the data,times of submission, or the like; persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which expert data from one or more tables may be linked and/orrelated to expert data in one or more other tables.

Still referring to FIG. 5, one or more database tables in prognosticlabel database 212 may include, as a non-limiting example, a sample datatable 500. Sample data table 500 may be a table listing sample data,along with, for instance, one or more linking columns to link such datato other information stored in prognostic label database 212. In anembodiment, sample data 504 may be acquired, for instance frombiological extraction database 200, in a raw or unsorted form, and maybe translated into standard forms, such as standard units ofmeasurement, labels associated with particular physiological datavalues, or the like; this may be accomplished using a datastandardization module 508, which may perform unit conversions. Datastandardization module 508 may alternatively or additionally map textualinformation, such as labels describing values tested for or the like,using language processing module 128 or equivalent components and/oralgorithms thereto.

Continuing to refer to FIG. 5, prognostic label database 212 may includea sample label table 512; sample label table 512 may list prognosticlabels received with and/or extracted from physiological samples, forinstance as received in the form of sample text 516. A languageprocessing module 128 may compare textual information so received toprognostic labels and/or form new prognostic labels according to anysuitable process as described above. Sample prognostic link table maycombine samples with prognostic labels, as acquired from sample labeltable and/or expert knowledge database 204; combination may be performedby listing together in rows or by relating indices or common columns oftwo or more tables to each other. Tables presented above are presentedfor exemplary purposes only; persons skilled in the art will be aware ofvarious ways in which data may be organized in expert knowledge database204 consistently with this disclosure.

Referring again to FIG. 2, first training set 112 may be populated byretrieval of one or more records from biological extraction database 200and/or prognostic label database 212; in an embodiment, entriesretrieved from biological extraction database 200 and/or prognosticlabel database 212 may be filtered and or select via query to match oneor more additional elements of information as described above, so as toretrieve a first training set 112 including data belonging to a givencohort, demographic population, or other set, so as to generate outputsas described below that are tailored to a person or persons with regardto whom system 100 classifies physiological samples to prognostic labelsas set forth in further detail below. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which records may be retrieved from biological extraction database200 and/or prognostic label database to generate a first training set toreflect individualized group data pertaining to a person of interest inoperation of system and/or method, including without limitation a personwith regard to whom at least a physiological sample is being evaluatedas described in further detail below. Diagnostic engine 108 mayalternatively or additionally receive a first training set 112 and storeone or more entries in biological extraction database 200 and/orprognostic label database 212 as extracted from elements of firsttraining set 112.

Still referring to FIG. 2, system 100 may include or communicate with anameliorative process label database 216; an ameliorative process labeldatabase 216 may include any data structure and/or datastore suitablefor use as a biological extraction database 200 as described above. Anameliorative process label database 216 may include one or more entrieslisting labels associated with one or more ameliorative processes asdescribed above, including any ameliorative labels correlated withprognostic labels in second training set 132 as described above;ameliorative process labels may be linked to or refer to entries inprognostic label database 212 to which ameliorative process labelscorrespond. Linking may be performed by reference to historical dataconcerning prognostic labels, such as therapies, treatments, and/orlifestyle or dietary choices chosen to alleviate conditions associatedwith prognostic labels in the past; alternatively or additionally, arelationship between an ameliorative process label and a data entry inprognostic label database 212 may be determined by reference to a recordin an expert knowledge database 204 linking a given ameliorative processlabel to a given category of prognostic label as described above.Entries in ameliorative process label database 212 may be associatedwith one or more categories of prognostic labels as described above, forinstance using data stored in and/or extracted from an expert knowledgedatabase 204.

Referring now to FIG. 6, an exemplary embodiment of an ameliorativeprocess label database 216 is illustrated. Ameliorative process labeldatabase 216 may, as a non-limiting example, organize data stored in theameliorative process label database 216 according to one or moredatabase tables. One or more database tables may be linked to oneanother by, for instance, common column values. For instance, a commoncolumn between two tables of ameliorative process label database 216 mayinclude an identifier of an expert submission, such as a form entry,textual submission, expert paper, or the like, for instance as definedbelow; as a result, a query may be able to retrieve all rows from anytable pertaining to a given submission or set thereof. Other columns mayinclude any other category usable for organization or subdivision ofexpert data, including types of expert data, names and/or identifiers ofexperts submitting the data, times of submission, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which expert data from one or more tablesmay be linked and/or related to expert data in one or more other tables.

Still referring to FIG. 6, ameliorative process label database 216 mayinclude a prognostic link table 600; prognostic link table may linkameliorative process data to prognostic label data, using any suitablemethod for linking data in two or more tables as described above.Ameliorative process label database 216 may include an ameliorativenutrition table 604, which may list one or more ameliorative processesbased on nutritional instructions, and/or links of such one or moreameliorative processes to prognostic labels, for instance as provided byexperts according to any method of processing and/or entering expertdata as described above, and/or using one or more machine-learningprocesses as set forth in further detail below. As a further example anameliorative action table 608 may list one or more ameliorativeprocesses based on instructions for actions a user should take,including without limitation exercise, meditation, and/or cessation ofharmful eating, substance abuse, or other habits, and/or links of suchone or more ameliorative processes to prognostic labels, as provided byexperts according to any method of processing and/or entering expertdata as described above and/or using one or more machine-learningprocesses as set forth in further detail below. As an additionalexample, an ameliorative supplement table 612 may list one or moreameliorative processes based on nutritional supplements, such as vitaminpills or the like, and/or links of such one or more ameliorativeprocesses to prognostic labels, as provided by experts according to anymethod of processing and/or entering expert data as described aboveand/or using one or more machine-learning processes as set forth infurther detail below. As a further non-limiting example, an ameliorativemedication table 616 may list one or more ameliorative processes basedon medications, including without limitation over-the-counter andprescription pharmaceutical drugs, and/or links of such one or moreameliorative processes to prognostic labels, as provided by expertsaccording to any method of processing and/or entering expert data asdescribed above and/or using one or more machine-learning processes asset forth in further detail below. As an additional example, acounterindication table 620 may list one or more counter-indications forone or more ameliorative processes; counterindications may include,without limitation allergies to one or more foods, medications, and/orsupplements, side-effects of one or more medications and/or supplements,interactions between medications, foods, and/or supplements, exercisesthat should not be used given one or more medical conditions, injuries,disabilities, and/or demographic categories, or the like; this may beacquired using expert submission as described above and/or using one ormore machine-learning processes as set forth in further detail below.Tables presented above are presented for exemplary purposes only;persons skilled in the art will be aware of various ways in which datamay be organized in ameliorative process database 216 consistently withthis disclosure.

Referring again to FIG. 2, second training set 132 may be populated byretrieval of one or more records from prognostic label database 212and/or ameliorative process label database 216; in an embodiment,entries retrieved from prognostic label database 212 and/or ameliorativeprocess label database 216 may be filtered and or select via query tomatch one or more additional elements of information as described above,so as to retrieve a second training set 132 including data belonging toa given cohort, demographic population, or other set, so as to generateoutputs as described below that are tailored to a person or persons withregard to whom system 100 classifies prognostic labels to ameliorativeprocess labels as set forth in further detail below. Persons skilled inthe art, upon reviewing the entirety of this disclosure, will be awareof various ways in which records may be retrieved from prognostic labeldatabase 212 and/or ameliorative process label database 216 to generatea second training set 132 to reflect individualized group datapertaining to a person of interest in operation of system and/or method,including without limitation a person with regard to whom at least aphysiological sample is being evaluated as described in further detailbelow. Diagnostic engine 108 may alternatively or additionally receive asecond training set 132 and store one or more entries in prognosticlabel database 212 and/or ameliorative process label database 216 asextracted from elements of second training set 132.

In an embodiment, and still referring to FIG. 2, diagnostic engine 108may receive an update to one or more elements of data represented infirst training set 112 and/or second training set 132, and may performone or more modifications to first training set 112 and/or secondtraining set 132, or to biological extraction database 200, expertknowledge database 204, prognostic label database 212, and/orameliorative process label database 216 as a result. For instance, aphysiological sample may turn out to have been erroneously recorded;diagnostic engine 108 may remove it from first training set 112, secondtraining set 132, biological extraction database 200, expert knowledgedatabase 204, prognostic label database 212, and/or ameliorative processlabel database 216 as a result. As a further example, a medical and/oracademic paper, or a study on which it was based, may be revoked;diagnostic engine 108 may remove it from first training set 112, secondtraining set 132, biological extraction database 200, expert knowledgedatabase 204, prognostic label database 212, and/or ameliorative processlabel database 216 as a result. Information provided by an expert maylikewise be removed if the expert loses credentials or is revealed tohave acted fraudulently.

Continuing to refer to FIG. 2, elements of data first training set 112,second training set 132, biological extraction database 200, expertknowledge database 204, prognostic label database 212, and/orameliorative process label database 216 may have temporal attributes,such as timestamps; diagnostic engine 108 may order such elementsaccording to recency, select only elements more recently entered forfirst training set 112 and/or second training set 132, or otherwise biastraining sets, database entries, and/or machine-learning models asdescribed in further detail below toward more recent or less recententries. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various ways in which temporal attributesof data entries may be used to affect results of methods and/or systemsas described herein.

Referring now to FIG. 7, machine-learning algorithms used by prognosticlabel learner 152 may include supervised machine-learning algorithms,which may, as a non-limiting example be executed using a supervisedlearning module 700 executing on diagnostic engine 108 and/or on anothercomputing device in communication with diagnostic engine 108, which mayinclude any hardware or software module. Supervised machine learningalgorithms, as defined herein, include algorithms that receive atraining set relating a number of inputs to a number of outputs, andseek to find one or more mathematical relations relating inputs tooutputs, where each of the one or more mathematical relations is optimalaccording to some criterion specified to the algorithm using somescoring function. For instance, a supervised learning algorithm may useelements of physiological data as inputs, prognostic labels as outputs,and a scoring function representing a desired form of relationship to bedetected between elements of physiological data and prognostic labels;scoring function may, for instance, seek to maximize the probabilitythat a given element of physiological state data 116 and/or combinationof elements of physiological data is associated with a given prognosticlabel and/or combination of prognostic labels to minimize theprobability that a given element of physiological state data 116 and/orcombination of elements of physiological state data 116 is notassociated with a given prognostic label and/or combination ofprognostic labels. Scoring function may be expressed as a risk functionrepresenting an “expected loss” of an algorithm relating inputs tooutputs, where loss is computed as an error function representing adegree to which a prediction generated by the relation is incorrect whencompared to a given input-output pair provided in first training set112. Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various possible variations of supervisedmachine learning algorithms that may be used to determine relationbetween elements of physiological data and prognostic labels. In anembodiment, one or more supervised machine-learning algorithms may berestricted to a particular domain for instance, a supervisedmachine-learning process may be performed with respect to a given set ofparameters and/or categories of parameters that have been suspected tobe related to a given set of prognostic labels, and/or are specified aslinked to a medical specialty and/or field of medicine covering aparticular set of prognostic labels. As a non-limiting example, aparticular set of blood test biomarkers and/or sensor data may betypically used by cardiologists to diagnose or predict variouscardiovascular conditions, and a supervised machine-learning process maybe performed to relate those blood test biomarkers and/or sensor data tothe various cardiovascular conditions; in an embodiment, domainrestrictions of supervised machine-learning procedures may improveaccuracy of resulting models by ignoring artifacts in training data.Domain restrictions may be suggested by experts and/or deduced fromknown purposes for particular evaluations and/or known tests used toevaluate prognostic labels. Additional supervised learning processes maybe performed without domain restrictions to detect, for instance,previously unknown and/or unsuspected relationships betweenphysiological data and prognostic labels.

Referring now to FIG. 8, ameliorative process label learner 160 may beconfigured to perform one or more supervised learning processes, asdescribed above; supervised learning processes may be performed by asupervised learning module 800 executing on diagnostic engine 108 and/oron another computing device in communication with diagnostic engine 108,which may include any hardware or software module. For instance, asupervised learning algorithm may use prognostic labels as inputs,ameliorative labels as outputs, and a scoring function representing adesired form of relationship to be detected between prognostic labelsand ameliorative labels; scoring function may, for instance, seek tomaximize the probability that a given prognostic label and/orcombination of prognostic labels is associated with a given ameliorativelabel and/or combination of ameliorative labels to minimize theprobability that a given prognostic label and/or combination ofprognostic labels is not associated with a given ameliorative labeland/or combination of ameliorative labels. In an embodiment, one or moresupervised machine-learning algorithms may be restricted to a particulardomain; for instance, a supervised machine-learning process may beperformed with respect to a given set of parameters and/or categories ofprognostic labels that have been suspected to be related to a given setof ameliorative labels, for instance because the ameliorative processescorresponding to the set of ameliorative labels are hypothesized orsuspected to have an ameliorative effect on conditions represented bythe prognostic labels, and/or are specified as linked to a medicalspecialty and/or field of medicine covering a particular set ofprognostic labels and/or ameliorative labels. As a non-limiting example,a particular set prognostic labels corresponding to a set ofcardiovascular conditions may be typically treated by cardiologists, anda supervised machine-learning process may be performed to relate thoseprognostic labels to ameliorative labels associated with varioustreatment options, medications, and/or lifestyle changes.

With continued reference to FIG. 8, ameliorative process label learner160 may perform one or more unsupervised machine-learning processes asdescribed above; unsupervised processes may be performed by anunsupervised learning module 804 executing on diagnostic engine 108and/or on another computing device in communication with diagnosticengine 108, which may include any hardware or software module. Forinstance, and without limitation, ameliorative process label learner 160and/or diagnostic engine 108 may perform an unsupervised machinelearning process on second training set 132, which may cluster data ofsecond training set 132 according to detected relationships betweenelements of the second training set 132, including without limitationcorrelations of prognostic labels to each other and correlations ofameliorative labels to each other; such relations may then be combinedwith supervised machine learning results to add new criteria forameliorative process label learner 160 to apply in relating prognosticlabels to ameliorative labels. As a non-limiting, illustrative example,an unsupervised process may determine that a first prognostic label 120correlates closely with a second prognostic label 136, where the firstprognostic label 120 has been linked via supervised learning processesto a given ameliorative label, but the second has not; for instance, thesecond prognostic label 136 may not have been defined as an input forthe supervised learning process, or may pertain to a domain outside of adomain limitation for the supervised learning process. Continuing theexample, a close correlation between first prognostic label 120 andsecond prognostic label 136 may indicate that the second prognosticlabel 136 is also a good match for the ameliorative label; secondprognostic label 136 may be included in a new supervised process toderive a relationship or may be used as a synonym or proxy for the firstprognostic label 120 by ameliorative process label learner 160.Unsupervised processes performed by ameliorative process label learner160 may be subjected to any domain limitations suitable for unsupervisedprocesses performed by prognostic label learner 152 as described above.

Still referring to FIG. 8, diagnostic engine 108 and/or ameliorativeprocess label learner 160 may detect further significant categories ofprognostic labels, relationships of such categories to ameliorativelabels, and/or categories of ameliorative labels using machine-learningprocesses, including without limitation unsupervised machine-learningprocesses as described above; such newly identified categories, as wellas categories entered by experts in free-form fields as described above,may be added to pre-populated lists of categories, lists used toidentify language elements for language learning module, and/or listsused to identify and/or score categories detected in documents, asdescribed above. In an embodiment, as additional data is added to system100, ameliorative process label learner 160 and/or diagnostic engine 108may continuously or iteratively perform unsupervised machine-learningprocesses to detect relationships between different elements of theadded and/or overall data; in an embodiment, this may enable system 100to use detected relationships to discover new correlations between knownbiomarkers, prognostic labels, and/or ameliorative labels and one ormore elements of data in large bodies of data, such as genomic,proteomic, and/or microbiome-related data, enabling future supervisedlearning and/or lazy learning processes to identify relationshipsbetween, e.g., particular clusters of genetic alleles and particularprognostic labels and/or suitable ameliorative labels. Use ofunsupervised learning may greatly enhance the accuracy and detail withwhich system may detect prognostic labels and/or ameliorative labels.

With continued reference to FIG. 8, ameliorative labels may be generatedbased on classification of the at least a prognostic output.Classification as used herein includes pairing or grouping prognosticoutputs as a function of some shared commonality. Prognostic outputs maybe grouped with certain endocrine disorders such as diabetes, metabolicsyndrome, and/or pre-diabetes which may generate an ameliorative labelassociated with a physical exercise recommendation that may includeaerobic exercises such as running, brisk walking, cycling, and/orswimming in an attempt to reduce elevated blood sugar levels in patientswith such endocrine disorders. Prognostic outputs grouped with certainalarm conditions such as chest pains, shortness of breath, cold sweat,and sudden dizziness may generate an ameliorative label associated withmedical tests, diagnostics, and/or procedures for a suspected myocardialinfarction such as an electrocardiogram (EKG), measurement of serumtroponin levels, complete blood count (CBC), chest x-ray,echocardiogram, cardiac CT, cardiac MRI, and/or coronarycatheterization. Ameliorative label may be generated based on groupingssuch as severity of prognostic output. For example, a user who presentswith mild chest pain and some indigestion may be grouped to a categoryof prognostic labels that is serious but not alarming and may generatean ameliorative label that includes a blood test for troponin levels torule out a potential myocardial infarction. A user who presents withcrushing chest pain, tingling down one or both arms, shortness ofbreath, and cold and clammy skin may be grouped into a category of alarmso as to generate an ameliorative label that includes a cardiac CT orcardiac MRI to see if user is suffering from some type of coronaryocclusion and may be a candidate for a possible coronarycatheterization. In yet another non-limiting example, ameliorative labelmay be generated as a function of severity and/or progression ofprognostic output. For example, a prognostic label that includes adiagnosis of hypothyroidism as evidenced by a thyroid stimulating level(TSH) of 6.0 (normal range is 1.4-5.5) may generate an ameliorativelabel that includes 150 mcg per day of iodine supplementation to lowerTSH within normal limits due to mild TSH elevation and/or mildprogression of hypothyroidism. A prognostic output that includes adiagnosis of hypothyroidism as evidenced by a TSH of 15.0 may generatean ameliorative label that includes 300 mcg per day of iodinesupplementation as well as a prescription for a T-4 containingmedication such as Synthroid and a T-3 containing medication such asCytomel due to the more severe progression of hypothyroidism.Classification of at least a prognostic output may include staging of aprognostic label. Staging may include dividing a disease state orcondition into categories on a spectrum of disease progression andsymptomology. For example, a user with a prognostic output thatindicates peri-menopause as evidenced by increasing prevalence of hotflashes may generate an ameliorative label that includes arecommendation for supplementation with black cohosh, while a user witha prognostic output that indicates progression to menopause as evidencedby persistent hot flashes, night sweats, absence of menstruation, dryhair, and fatigue may generate an ameliorative label that containsrecommendations for supplementation with bio-identical hormonereplacement therapy such as estrone (E1), estradiol (E2), estriol (E3),progesterone, testosterone, dehydroepiandrosterone (DHEA), and/orpregnenolone. In yet another non-limiting example, early stage of adisease such as Alzheimer's disease as demonstrated by mild cognitiveimpairment may generate an ameliorative label that includes norecommended medical treatment except for watchful waiting. However,advanced Alzheimer's disease may warrant an ameliorative label thatincludes medical intervention and may require a prescription medication.Ameliorative label may be generated by any of the methodologies asdescribed in this disclosure.

Continuing to view FIG. 8, ameliorative process label learner 160 may beconfigured to perform a lazy learning process as a function of thesecond training set 132 and the at least a prognostic output to producethe at least an ameliorative output; a lazy learning process may includeany lazy learning process as described above regarding prognostic labellearner 152. Lazy learning processes may be performed by a lazy learningmodule 808 executing on diagnostic engine 108 and/or on anothercomputing device in communication with diagnostic engine 108, which mayinclude any hardware or software module. Ameliorative output 812 may beprovided to a user output device as described in further detail below.

In an embodiment, and still referring to FIG. 8, ameliorative processlabel learner 160 may generate a plurality of ameliorative labels havingdifferent implications for a particular person. For instance, where aprognostic label indicates that a person has a magnesium deficiency,various dietary choices may be generated as ameliorative labelsassociated with correcting the deficiency, such as ameliorative labelsassociated with consumption of almonds, spinach, and/or dark chocolate,as well as ameliorative labels associated with consumption of magnesiumsupplements. In such a situation, ameliorative process label learner 160and/or diagnostic engine 108 may perform additional processes to resolveambiguity. Processes may include presenting multiple possible results toa medical practitioner, informing the medical practitioner of variousoptions that may be available, and/or that follow-up tests, procedures,or counseling may be required to select an appropriate choice.Alternatively or additionally, processes may include additional machinelearning steps. For instance, ameliorative process label learner 160 mayperform one or more lazy learning processes using a more comprehensiveset of user data to identify a more probably correct result of themultiple results. Results may be presented and/or retained withrankings, for instance to advise a medical professional of the relativeprobabilities of various ameliorative labels being correct or idealchoices for a given person; alternatively or additionally, ameliorativelabels associated with a probability of success or suitability below agiven threshold and/or ameliorative labels contradicting results of theadditional process, may be eliminated. As a non-limiting example, anadditional process may reveal that a person is allergic to tree nuts,and consumption of almonds may be eliminated as an ameliorative label tobe presented.

Continuing to refer to FIG. 8, ameliorative process label learner 160may be designed and configured to generate further training data and/orto generate outputs using longitudinal data 816. As used herein,longitudinal data 816 may include a temporally ordered series of dataconcerning the same person, or the same cohort of persons; for instance,longitudinal data 816 may describe a series of blood samples taken oneday or one month apart over the course of a year. Longitudinal data 816may related to a series of samples tracking response of one or moreelements of physiological data recorded regarding a person undergoingone or more ameliorative processes linked to one or more ameliorativeprocess labels. Ameliorative process label learner 160 may track one ormore elements of physiological data and fit, for instance, a linear,polynomial, and/or splined function to data points; linear, polynomial,or other regression across larger sets of longitudinal data, using, forinstance, any regression process as described above, may be used todetermine a best-fit graph or function for the effect of a givenameliorative process over time on a physiological parameter. Functionsmay be compared to each other to rank ameliorative processes; forinstance, an ameliorative process associated with a steeper slope incurve representing improvement in a physiological data element, and/or ashallower slope in a curve representing a slower decline, may be rankedhigher than an ameliorative process associated with a less steep slopefor an improvement curve or a steeper slope for a curve marking adecline. Ameliorative processes associated with a curve and/or terminaldata point representing a value that does not associate with apreviously detected prognostic label may be ranked higher than one thatis not so associated. Information obtained by analysis of longitudinaldata 816 may be added to ameliorative process database and/or secondtraining set.

Referring now to FIG. 9, an exemplary embodiment of a plan generatormodule 168 is illustrated. Comprehensive instruction set 172 includes atleast a current prognostic descriptor 900 which as used in thisdisclosure is an element of data describing a current prognostic statusbased on at least one prognostic output. Plan generator module 168 mayproduce at least a current prognostic descriptor 900 using at least aprognostic output. In an embodiment, plan generator module 168 mayinclude a label synthesizer 904. Label synthesizer 904 may include anysuitable software or hardware module. In an embodiment, labelsynthesizer 904 may be designed and configured to combine a plurality oflabels in at least a prognostic output together to provide maximallyefficient data presentation. Combination of labels together may includeelimination of duplicate information. For instance, label synthesizer904 and/or a computing device 104 may be designed and configure todetermine a first prognostic label of the at least a prognostic label isa duplicate of a second prognostic label of the at least a prognosticlabel and eliminate the first prognostic label. Determination that afirst prognostic label is a duplicate of a second prognostic label mayinclude determining that the first prognostic label is identical to thesecond prognostic label; for instance, a prognostic label generated fromtest data presented in one biological extraction of at least abiological extraction may be the same as a prognostic label generatedfrom test data presented in a second biological extraction of at least abiological extraction. As a further non-limiting example, a firstprognostic label may be synonymous with a second prognostic label, wheredetection of synonymous labels may be performed, without limitation, bya language processing module 128 as described above.

Continuing to refer to FIG. 9, label synthesizer 904 may groupprognostic labels according to one or more classification systemsrelating the prognostic labels to each other. For instance, plangenerator module 168 and/or label synthesizer 904 may be configured todetermine that a first prognostic label of the at least a prognosticlabel and a second prognostic label of the at least a prognostic labelbelong to a shared category. A shared category may be a category ofconditions or tendencies toward a future condition to which each offirst prognostic label and second prognostic label belongs; as anexample, lactose intolerance and gluten sensitivity may each be examplesof digestive sensitivity, for instance, which may in turn share acategory with food sensitivities, food allergies, digestive disorderssuch as celiac disease and diverticulitis, or the like. Shared categoryand/or categories may be associated with prognostic labels as well. Agiven prognostic label may belong to a plurality of overlappingcategories. Plan generator module 168 may be configured to add acategory label associated with a shared category to comprehensiveinstruction set 172, where addition of the label may include addition ofthe label and/or a datum linked to the label, such as a textual ornarrative description. In an embodiment, relationships betweenprognostic labels and categories may be retrieved from a prognosticlabel classification database 908, for instance by generating a queryusing one or more prognostic labels of at least a prognostic output,entering the query, and receiving one or more categories matching thequery from the prognostic label classification database 908.

Referring now to FIG. 10, an exemplary embodiment of a prognostic labelclassification database 908 is illustrated. Prognostic labelclassification database 908 may be implemented as any database and/ordatastore suitable for use as biological extraction database 300 asdescribed above. One or more database tables in prognostic labelclassification database 908 may include, without limitation, asymptomatic classification table 1000; symptomatic classification table1000 may relate each prognostic label to one or more categories ofsymptoms associated with that prognostic label. As a non-limitingexample, symptomatic classification table 1000 may include recordsindicating that each of lactose intolerance and gluten sensitivityresults in symptoms including gas buildup, bloating, and abdominal pain.One or more database tables in prognostic label classification database908 may include, without limitation, a systemic classification table1004; systemic classification table 1004 may relate each prognosticlabel to one or more systems associated with that prognostic label. As anon-limiting example, systemic classification table 1004 may includerecords indicating each of lactose intolerance and gluten sensitivityaffects the digestive system; two digestive sensitivities linked toallergic or other immune responses may additionally be linked insystemic classification table 1004 to the immune system. One or moredatabase tables in prognostic label classification database 908 mayinclude, without limitation, a body part classification table 1008; bodypart classification table 1008 may relate each prognostic label to oneor more body parts associated with that prognostic label. As anon-limiting example, body part classification table 1008 may includerecords indicating each of psoriasis and rosacea affects the skin of aperson. One or more database tables in prognostic label classificationdatabase 908 may include, without limitation, a causal classificationtable 1112; causal classification table 1112 may relate each prognosticlabel to one or more causes associated with that prognostic label. As anon-limiting example, causal classification table 1112 may includerecords indicating each of type 2 diabetes and hypertension may haveobesity as a cause. The above-described tables, and entries therein, areprovided solely for exemplary purposes. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variousadditional examples for tables and/or relationships that may be includedor recorded in prognostic classification table consistently with thisdisclosure.

Referring again to FIG. 9, plan generator module 168 may be configuredto generate current prognostic descriptor 900 by converting one or moreprognostic labels into narrative language. As a non-limiting example,plan generator module 168 may include a narrative language unit 912,which may be configured to determine an element of narrative languageassociated with at least a prognostic label and include the element ofnarrative language in current prognostic label descriptor. Narrativelanguage unit 912 may implement this, without limitation, by using alanguage processing module 128 to detect one or more associationsbetween prognostic labels, or lists of prognostic labels, and phrasesand/or statements of narrative language. Alternatively or additionally,narrative language unit 912 may retrieve one or more elements ofnarrative language from a narrative language database 916, which maycontain one or more tables associating prognostic labels and/or groupsof prognostic labels with words, sentences, and/or phrases of narrativelanguage. One or more elements of narrative language may be included incomprehensive instruction set 172, for instance for display to a user astext describing a current prognostic status of the user. Currentprognostic descriptor 900 may further include one or more images; one ormore images may be retrieved by plan generator module 168 from an imagedatabase 920, which may contain one or more tables associatingprognostic labels, groups of prognostic labels, current prognosticdescriptors 1000, or the like with one or more images.

With continued reference to FIG. 9, comprehensive instruction set 172may include one or more follow-up suggestions, which may include,without limitation, suggestions for acquisition of an additionalbiological extraction; in an embodiment, additional biologicalextraction may be provided to diagnostic engine 108, which may triggerrepetition of one or more processes as described above, includingwithout limitation generation of prognostic output, refinement orelimination of ambiguous prognostic labels of prognostic output,generation of ameliorative output, and/or refinement or elimination ofambiguous ameliorative labels of ameliorative output. For instance,where a pegboard test result suggests possible diagnoses of Parkinson'sdisease, Huntington's disease, ALS, and MS as described above, follow-upsuggestions may include suggestions to perform endocrinal tests, genetictests, and/or electromyographic tests; results of such tests mayeliminate one or more of the possible diagnoses, such that asubsequently displayed output only lists conditions that have not beeneliminated by the follow-up test. Follow-up tests may include anyreceipt of any biological extraction as described above.

With continued reference to FIG. 9, comprehensive instruction set mayinclude one or more elements of contextual information, includingwithout limitation any patient medical history such as current labresults, a current reason for visiting a medical professional, currentstatus of one or more currently implemented treatment plans,biographical information concerning the patient, and the like. One ormore elements of contextual information may include goals a patientwishes to achieve with a medical visit or session, and/or as result ofinteraction with diagnostic engine 108. Contextual information mayinclude one or more questions a patient wishes to have answered in amedical visit and/or session, and/or as a result of interaction withdiagnostic engine 108. Contextual information may include one or morequestions to ask a patient. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various forms ofcontextual information that may be included, consistently with thisdisclosure.

With continued reference to FIG. 9, comprehensive instruction set 172may include at least a future prognostic descriptor 924. As used herein,a future prognostic descriptor 924 is an element of data describing afuture prognostic status based on at least one prognostic output, whichmay include without limitation a desired further prognostic status. Inan embodiment, future prognostic descriptor 924 may include any elementsuitable for inclusion in current prognostic descriptor 900. Futureprognostic descriptor 924 may be generated using any processes, modules,and/or components suitable for generation of current prognosticdescriptor 900 as described above.

Still referring to FIG. 9, comprehensive instruction set 172 includes atleast an ameliorative process descriptor 1028, which as defined in thisdisclosure an element of data describing one or more ameliorativeprocesses to be followed based on at least one ameliorative output; atleast an ameliorative process descriptor 1028 may include descriptorsfor ameliorative processes usable to achieve future prognosticdescriptor 924. Plan generator module 168 may produce at least anameliorative process descriptor 1028 using at least a prognostic output.In an embodiment, label synthesizer 904 may be designed and configuredto combine a plurality of labels in at least an ameliorative outputtogether to provide maximally efficient data presentation. Combinationof labels together may include elimination of duplicate information. Forinstance, label synthesizer 904 and/or a computing device 104 may bedesigned and configure to determine a first ameliorative label of the atleast an ameliorative label is a duplicate of a second ameliorativelabel of the at least an ameliorative label and eliminate the firstameliorative label. Determination that a first ameliorative label is aduplicate of a second ameliorative label may include determining thatthe first ameliorative label is identical to the second ameliorativelabel; for instance, a ameliorative label generated from test datapresented in one biological extraction of at least a biologicalextraction may be the same as a ameliorative label generated from testdata presented in a second biological extraction of at least abiological extraction. As a further non-limiting example, a firstameliorative label may be synonymous with a second ameliorative label,where detection of synonymous labels may be performed, withoutlimitation, by a language processing module 128 as described above.

Continuing to refer to FIG. 9, label synthesizer 904 may groupameliorative labels according to one or more classification systemsrelating the ameliorative labels to each other. For instance, plangenerator module 168 and/or label synthesizer 904 may be configured todetermine that a first ameliorative label of the at least anameliorative label and a second ameliorative label of the at least anameliorative label belong to a shared category. A shared category may bea category of conditions or tendencies toward a future condition towhich each of first ameliorative label and second ameliorative labelbelongs; as an example, lactose intolerance and gluten sensitivity mayeach be examples of digestive sensitivity, for instance, which may inturn share a category with food sensitivities, food allergies, digestivedisorders such as celiac disease and diverticulitis, or the like. Sharedcategory and/or categories may be associated with ameliorative labels aswell. A given ameliorative label may belong to a plurality ofoverlapping categories. Plan generator module 168 may be configured toadd a category label associated with a shared category to comprehensiveinstruction set 172, where addition of the label may include addition ofthe label and/or a datum linked to the label, such as a textual ornarrative description. In an embodiment, relationships betweenameliorative labels and categories may be retrieved from an ameliorativelabel classification database 932, for instance by generating a queryusing one or more ameliorative labels of at least an ameliorativeoutput, entering the query, and receiving one or more categoriesmatching the query from the ameliorative label classification database932.

Referring now to FIG. 11, an exemplary embodiment of an ameliorativelabel classification database 932 is illustrated. Ameliorative labelclassification database 932 may be implemented as any database and/ordatastore suitable for use as biological extraction database 300 asdescribed above. One or more database tables in ameliorative labelclassification database 932 may include, without limitation, anintervention category table 1100; intervention 1200 may relate eachameliorative label to one or more categories associated with thatameliorative label. As a non-limiting example, intervention categorytable 1100 may include records indicating that each of a plan to consumea given quantity of almonds and a plan to consume less meat maps to acategory of nutritional instruction, while a plan to jog for 30 minutesper day maps to a category of activity. One or more database tables inameliorative label classification database 932 may include, withoutlimitation, a nutrition category table 1104; nutrition category table1104 may relate each ameliorative label pertaining to nutrition to oneor more categories associated with that ameliorative label. As anon-limiting example, nutrition category table 1104 may include recordsindicating that each of a plan to consume more almonds and a plan toconsume more walnuts qualifies as a plan to consume more nuts, as wellas a plan to consume more protein. One or more database tables inameliorative label classification database 932 may include, withoutlimitation, an action category table 1108; action category table 1108may relate each ameliorative label pertaining to an action to one ormore categories associated with that ameliorative label. As anon-limiting example, action category table 1108 may include recordsindicating that each of a plan jog for 30 minutes a day and a plan toperform a certain number of sit-ups per day qualifies as an exerciseplan. One or more database tables in ameliorative label classificationdatabase 932 may include, without limitation, a medication categorytable 1112; medication category table 1112 may relate each ameliorativelabel associated with a medication to one or more categories associatedwith that ameliorative label. As a non-limiting example, medicationcategory table 1112 may include records indicating that each of a planto take an antihistamine and a plan to take an anti-inflammatory steroidbelongs to a category of allergy medications. One or more databasetables in ameliorative label classification database 932 may include,without limitation, a supplement category table 1116; supplementcategory table 1116 may relate each ameliorative label pertaining to asupplement to one or more categories associated with that ameliorativelabel. As a non-limiting example, supplement category table 1116 mayinclude records indicating that each of a plan to consume a calciumsupplement and a plan to consume a vitamin D supplement corresponds to acategory of supplements to aid in bone density. Ameliorative labels maybe mapped to each of nutrition category table 1104, action categorytable 1108, supplement category table 1116, and medication categorytable 1112 using intervention category table 1100. The above-describedtables, and entries therein, are provided solely for exemplary purposes.Persons skilled in the art, upon reviewing the entirety of thisdisclosure, will be aware of various additional examples for tablesand/or relationships that may be included or recorded in ameliorativeclassification table consistently with this disclosure.

Referring again to FIG. 9, plan generator module 168 may be configuredto generate ameliorative process descriptor 1028 by converting one ormore ameliorative labels into narrative language. As a non-limitingexample, plan generator module 168 may include a narrative language unit912, which may be configured to determine an element of narrativelanguage associated with at least an ameliorative label and include theelement of narrative language in current ameliorative label descriptor.Narrative language unit 912 may implement this, without limitation, byusing a language processing module 128 to detect one or moreassociations between ameliorative labels, or lists of ameliorativelabels, and phrases and/or statements of narrative language.Alternatively or additionally, narrative language unit 912 may retrieveone or more elements of narrative language from narrative languagedatabase 916, which may contain one or more tables associatingameliorative labels and/or groups of ameliorative labels with words,sentences, and/or phrases of narrative language. One or more elements ofnarrative language may be included in comprehensive instruction set 172,for instance for display to a user as text describing a currentameliorative status of the user. Ameliorative process descriptor 1028may further include one or more images; one or more images may beretrieved by plan generator module 168 from an image database 920, whichmay contain one or more tables associating ameliorative labels, groupsof ameliorative labels, ameliorative process descriptors 1028, or thelike with one or more images.

Referring now to FIG. 12, and exemplary embodiment of a narrativelanguage database 916 is illustrated. Narrative language database 916may be implemented as any database and/or datastore suitable for use asbiological extraction database 300 as described above. One or moredatabase tables in narrative language database 916 may include, withoutlimitation, a prognostic description table 1200, which may linkprognostic labels to narrative descriptions associated with prognosticlabels. One or more database tables in narrative language database 916may include, without limitation, an ameliorative description table 1204,which may link ameliorative process labels to narrative descriptionsassociated with ameliorative process labels. One or more database tablesin narrative language database 916 may include, without limitation, acombined description table 1208, which may link combinations ofprognostic labels and ameliorative labels to narrative descriptionsassociated with the combinations. One or more database tables innarrative language database 916 may include, without limitation, aparagraph template table 1212, which may contain one or more templatesof paragraphs, pages, reports, or the like into which images and text,such as images obtained from image database 920 and text obtained fromprognostic description table 1200, ameliorative description table 1204,and combined description table 1208 may be inserted. Tables in narrativedescription table 1016 may be populated, as a non-limiting example,using submissions from experts, which may be collected according to anyprocesses described above. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various way sin whichentries in narrative description table 1016 may be categorized and/ororganized.

Referring now to FIG. 13, an exemplary embodiment of an image database920 is illustrated. Image database 920 may be implemented as anydatabase and/or datastore suitable for use as biological extractiondatabase 300 as described above. One or more database tables in imagedatabase 102 may include, without limitation, a prognostic image table1300, which may link prognostic labels to images associated withprognostic labels. One or more database tables in image database 920 mayinclude, without limitation, an ameliorative image table 1304, which maylink ameliorative process labels to images associated with ameliorativeprocess labels. One or more database tables in image database 920 mayinclude, without limitation, a combined description table 1408, whichmay link combinations of prognostic labels and ameliorative labels toimages associated with the combinations. One or more database tables inimage database 102 may include, without limitation, a prognostic videotable 1312, which may link prognostic labels to videos associated withprognostic labels. One or more database tables in image database 920 mayinclude, without limitation, an ameliorative video table 1316, which maylink ameliorative process labels to videos associated with ameliorativeprocess labels. One or more database tables in image database 920 mayinclude, without limitation, a combined video table 1320, which may linkcombinations of prognostic labels and ameliorative labels to videosassociated with the combinations. Tables in image database 920 may bepopulated, without limitation, by submissions by experts, which may beprovided according to any process or process steps described in thisdisclosure for collection of expert submissions.

Referring again to FIG. 9, plan generator module 168 may be configuredto receive at least an element of user data and filter diagnostic outputusing the at least an element of user data. At least an element of userdata, as used herein, is any element of data describing the user, userneeds, and/or user preferences. At least an element of user data mayinclude a constitutional restriction. At least a constitutionalrestriction may include any health-based reason that a user may beunable to engage in a given ameliorative process; at least aconstitutional restriction may include any counter-indication asdescribed above, including an injury, a diagnosis of somethingpreventing use of one or more ameliorative processes, an allergy orfood-sensitivity issue, a medication that is counter-indicated, or thelike. At least an element of user data may include at least a userpreference. At least a user preference may include, without limitation,any preference to engage in or eschew any ameliorative process and/orother potential elements of a comprehensive instruction set 172,including religious preferences such as forbidden foods, medicalinterventions, exercise routines, or the like.

Referring to FIG. 14, an exemplary embodiment of a user database 936 isillustrated. User database 936 may be implemented as any database and/ordatastore suitable for use as biological extraction database 300 asdescribed above. One or more database tables in user database 936 mayinclude, without limitation, a constitution restriction table 1500; atleast a constitutional restriction may be linked to a given user and/oruser identifier in a constitutional restriction table 1400. One or moredatabase tables in user database 936 may include, without limitation, auser preference table 1404; at least a user preference may be linked toa given user and/or user identifier in a user preference table 1404.

Referring now to FIG. 15, an exemplary embodiment of an advisory module184 is illustrated. Advisory module 184 may be configured to generate anadvisor instruction set 1600 as a function of the diagnostic output.Advisory instruction set 1500 may contain any element suitable forinclusion in comprehensive instruction set 172; advisory instruction set1500 and/or any element thereof may be generated using any processsuitable for generation of comprehensive instruction set 172. Advisoryinstruction set 1500 may include one or more specialized instructions1504; specialized instructions, as used herein, are instructions thecontents of which are selected for display to a particular informedadvisor. Selection of instructions for a particular informed advisor maybe obtained, without limitation, from information concerning theparticular informed advisor, which may be retrieved from a user database936 or the like. As a non-limiting example, where an informed advisor isa doctor, specialized instruction 1504 may include data from biologicalextraction as described above; specialized instruction may include oneor more medical records of user, which may, as a non-limiting example,be downloaded or otherwise received from an external database containingmedical records and/or a database (not shown) operating on a computingdevice 104. As a further non-limiting example medical data relevant tonutrition, such as blood sugar, potassium level, or any othermeasurement serving as an indicator of user nutrition may be provided toan informed advisor whose role is as an alimentary instructor, coach, orthe like.

In an embodiment, and continuing to refer to FIG. 15, advisory module184 may be configured to receive at least an advisory input from theadvisor client device 188. At least an advisory input may include anyinformation provided by an informed advisor via advisor client device188. Advisory input may include medical information and/or advice.Advisory input may include user data, including user habits,preferences, religious affiliations, constitutional restrictions, or thelike. Advisory input may include spiritual and/or religious advice.Advisory input may include user-specific diagnostic information.Advisory input may be provided to user client device 180; alternativelyor additionally, advisory input may be fed back into system 100,including without limitation insertion into user database 936, inclusionin or use to update diagnostic engine 108, for instance by augmentingmachine-learning models and/or modifying machine-learning outputs via alazy-learning protocol or the like as described above.

With continued reference to FIG. 15, advisory module 184 may include anartificial intelligence advisor 1508 configured to perform a usertextual conversation with the user client device 180. Artificialintelligence advisor 1508 may provide output to advisor client device188 and/or user client device 180. Artificial intelligence advisor 1508may receive inputs from advisor client device 188 and/or user clientdevice 180. Inputs and/or outputs may be exchanged using messagingservices and/or protocols, including without limitation any instantmessaging protocols. Persons skilled in the art, up reviewing theentirety of this disclosure, will be aware of a multiplicity ofcommunication protocols that may be employed to exchange text messagesas described herein. Text messages may be provided in textual formand/or as audio files using, without limitation, speech-to-text and/ortext-to-speech algorithms.

With continued reference to FIG. 15, advisory module 184 may output,with advisory output, a textual entry field 1512. Textual entry field1512 may include a searchable input field that allows entry of a searchterm such as a word or phrase to be entered by a user such as aninformed advisor. In an embodiment, textual entry field 1512 may allowfor entry of a search term to be matched with labels contained withinthe at least at diagnostic output. For example, an informed advisor suchas a medical professional may enter into a search term a results of afasting glucose test after receiving at least a diagnostic output ofdiabetes. In such an instance, user such as an informed advisor may beable to search multiple results such as fasting glucose test levelsrecorded over a certain period of time such as several years and/ormonths. In yet another non-limiting example, an informed advisor such asan alimentary professional may search for user's most recent exerciselog and/or nutrition records. In yet another non-limiting example, aninformed advisor such as a nurse practitioner may enter information intotextual entry field 1512 to search for information pertaining to user'smedication history after receiving at least a diagnostic output of acutekidney injury. In an embodiment, textual entry field 1512 may allow auser such as an informed advisor to navigate different areas of advisoryoutput. For example, an informed advisor may utilize textual entry field1512 to navigate to different locations such as a table of contents, andor sections organized into different categories as described in moredetail below.

With continued reference to FIG. 15, advisory module 184 may include inan advisory output a category field 1516. Category field 1516 mayinclude a textual field that contains advisory output organized intocategories. Category, as used herein, is any breakdown of advisoryoutput by shared characteristics. Categories may include for example,breakdown by informed advisor type. For example, informed advisors maybe categorized into categories of expertise such as spiritualprofessionals, nutrition professionals, fitness professionals and thelike. Categories may include sub-categories of specialties such as forexample functional medicine informed advisors may be organized intosub-categories based on body system they may be treating. This couldinclude sub-categories such as dermatology specialists, Genito-urologyspecialists, gastroenterology specialists, neurology specialists and thelike. Categories may include a breakdown by time such as chronologicalorder and/or reverse chronological order. Categories may be modifiedand/or organized into test results such as for example all completeblood counts that a user has ever had performed may be located in onecategory, and all CT scans that a user has had performed may be locatedin another category. Categories may include a breakdown by relevance,such as highly relevant test results and/or test results that areoutside normal limits.

With continued reference to FIG. 15, advisory module 184 may include inan advisory output a relevance field 1520. Relevance field 1520 as usedherein is a textual field that contains advisory output informationlabeled as being relevant. Relevance, as used herein, is any informationcontained within advisory output that is closely connected and/orrelated to diagnostic output. Relevance may include information thatwould be of interest to a particular category of informed advisor. Forexample, an informed advisor such as an ophthalmologist may deeminformation contained within at least an advisory output such as ameasurement of a user's intra-ocular pressure to be of relevance, whilean advisory output containing information summarizing a user's lastappointment with a podiatrist to not be of relevance. In yet anothernon-limiting example, an informed advisor such as an alimentaryprofessional may deem information contained within an advisory outputsuch as a summary of a user's last appointment with an orthopedic doctorto be relevant while a summary of a user's last colonoscopy may not berelevant. In an embodiment, relevance may be viewed on a continuum.Information contained within at least an advisory output that directlyrelates to an informed advisor and is of high probative value to aninformed advisor may be highly relevant. For example, a nutritionist maydeem a journal of a user's eating habits as highly relevant. In yetanother non-limiting example, a spiritual professional may deem asummary of a user's church patterns as highly relevant. Information thatis related to an informed advisor but does not directly affect aninformed advisor may be moderately relevant. For example, adermatologist may deem information pertaining to a user's last physicalexam with an internal medicine doctor to be moderately relevant. In yetanother non-limiting example, an endocrinologist may deem informationpertaining to a user's last appointment with a podiatrist to bemoderately relevant for a user diagnosed with diabetes. Information thatis not related to an informed advisor and does not affect an informedadvisor may be of low relevance. For example, a trauma surgeon may deeminformation about a user's last dental cleaning to be of low relevance.In yet another non-limiting example, a cardiologist may deem informationabout a user's last bone density scan to be of low relevance. In anembodiment, user such as informed advisor may use textual entry field1512 to navigate advisory output to find information that is relevant.In an embodiment, information contained within at least an advisoryoutput may be marked as relevant such as by another informed advisor.For example, a functional medicine doctor may mark an elevated fastingblood glucose level as relevant before transmitting such a result to anutrition professional.

In an embodiment, and still referring to FIG. 15, a relevance field 1520may include an image, link, or other visual element that an informedadvisor may select or otherwise interact with to expand or contract aportion of advisory output; for instance, relevance field 1520 mayinclude a symbol next to or on a section heading that can cause acorresponding section of text to display when activated a first time anddisappear when activated a second time. As a result, an informed advisormay be presented initially with some text visible and other text notvisible; initial presentation may hide all text but section headers.Alternatively or additionally, where informed advisor belongs to aparticular category of informed advisor and/or has a profile in, forinstance, advisory database 1524 indicating categories of interest tothe informed advisor, sections of text and/or images related to suchcategories may initially display while other sections do not displayunless a relevance field 1520 corresponding to such sections is selectedby the informed advisor.

With continued reference to FIG. 15, advisory module 184 containsadvisory database 1524. Advisory database 1524 may be implemented as anydatabase and/or datastore suitable for use as an advisory database. Anexemplary embodiment of an advisory database 1524 is provided below inFIG. 17.

Referring now to FIG. 16, an exemplary embodiment of an artificialintelligence advisor 1508 is illustrated. Artificial intelligenceadvisor 1508 may include a user communication learner 1600. Usercommunication learner 1600 may be any form of machine-learning learneras described above, implementing any form of language processing and/ormachine learning. In an embodiment, user communication learner 1600 mayinclude a general learner 1604; general learner 1604 may be a learnerthat derives relationships between user inputs and correct outputs usinga training set that includes, without limitation, a corpus of previousconversations. Corpus of previous conversations may be logged by acomputing device 104 as conversations take place; user feedback, and/orone or more functions indicating degree of success of a conversation maybe used to differentiate between positive input-output pairs to use fortraining and negative input-output pairs not to use for training.Outputs may include textual strings and/or outputs from any databases,modules, and/or learners as described in this disclosure, includingwithout limitation prognostic labels, prognostic descriptors,ameliorative labels, ameliorative descriptors, user information, or thelike; for instance, general learner 1604 may determine that some inputsoptimally map to textual response outputs, while other inputs map tooutputs created by retrieval of module and/or database outputs, such asretrieval of prognostic descriptors, ameliorative descriptors, or thelike. User communication learner may include a user-specific learner1608, which may generate one or more modules that learn input-outputpairs pertaining to communication with a particular user; a userspecific learner 1708 may initially use input-output pairs establishedby general learner 1604 and may modify such pairs to match optimalconversation with the particular user by iteratively minimizing an errorfunction.

Still referring to FIG. 16, general learner 1604 and/or user-specificlearner 1608 may initialize, prior to training, using one or more recordretrieved from a default response database 1612. Default responsedatabase 1612 may link inputs to outputs according to initialrelationships entered by users, including without limitation experts asdescribed above, and/or as created by a previous instance or version ofgeneral learner 1604 and/or user-specific learner 1608. Default responsedatabase 1612 may periodically be updated with information from newlygenerated instances of general learner 1604 and/or user-specific learner1608. Inputs received by artificial intelligence advisor 1508 may bemapped to canonical and/or representative inputs by synonym detection asperformed, for instance, by a language processing module 128; languageprocessing module 128 may be involved in textual analysis and/orgeneration of text at any other point in machine-learning and/orcommunication processes undergone by artificial intelligence advisor1508.

With continued reference to FIG. 16, user-specific learner 1608 may beconfigured to detect a nutritional advisory intervention event. Anutritional advisory intervention event includes any of the nutritionaladvisory intervention events as described above in more detail.User-specific learner 1608 may receive a plurality of nutritional inputsidentifying user nutritional behavior. User-specific learner 1608 mayreceive a nutritional input from user client device utilizing anynetwork methodology as described herein. A “user nutritional behavior,”as used in this disclosure, is data describing any user behaviorrelating to the acquisition of and/or consumption of nourishment,including food and/or supplements. User behavior may describe one ormore eating patterns, times of the day when a user may have a meal,nutrients and/or foods that the user consumes, nutrients and/or foodsthe user should not consume, supplements the user consumes, supplementsthe user should not consume and the like. For instance and withoutlimitation, user behavior may describe five different meals a user mayconsume for breakfast. In yet another non-limiting example, userbehavior may describe a list of foods the user should not consume whilethe user maintains a certain style of eating, such as a user who isfollowing a paleo style of eating and cannot consume any grains.User-specific learner 1608 identifies a nutritional behavior outlier. A“nutritional behavior outlier,” as used in this disclosure, is anynutritional behavior that falls outside of a user's standard behaviorpatterns, and may indicate that a user is not being compliant with aninstruction set, such as a comprehensive instruction set. User-specificlearner 1608 may identify nutritional behavior outliers contained withina plurality of nutritional inputs. User-specific learner 1608 mayinitiate a consultation event with a nutritional informed advisor uponidentifying a nutritional behavior outlier. A consultation event mayinclude any consultation event as described herein. For instance andwithout limitation, user-specific learner 1608 may receive a pluralityof nutritional inputs identifying one or more meals a user may haveconsumed over the past seven days. User-specific learner 1608 mayidentify a nutritional behavior outlier such as meals over the course offour days that identify the user has not been compliant with anightshade free diet. In such an instance, user-specific learner 1608may initiate a consultation event with a nutritional informed advisorsuch as a dietician, who may reach out to the user to have a phone callto inquire why the user has not been complaint with a nightshade freediet, and may provide words of encouragement and/or meal plans that mayencourage the user to reinitiate the nightshade free diet.

Referring now to FIG. 17, an exemplary embodiment of advisory database1524 is illustrated. One or more database tables in advisory database1524 may link to data surrounding an informed advisor. Advisory database1524 may include one or more database tables categorized by expertise ofinformed advisor. One or more database tables in advisory database 1524may include, without limitation, an artificial intelligence informedadvisors table 1704, which may contain any and all informationpertaining to artificial intelligence informed advisors. One or moredatabase tables in advisory database 1524 may include, withoutlimitation, a spiritual professional informed advisors table 1708, whichmay contain any and all information pertaining to spiritual professionalinformed advisors. Spiritual professional informed advisors may includespiritual professionals who may participate in cultivating spiritualitythrough exercise of practices such as prayer, meditation, breath work,energy work, and the like. One or more database tables in advisorydatabase 1524 may include, without limitation, alimentary professionalinformed advisors table 1712, which may include any and all informationpertaining to alimentary informed advisors. Alimentary informed advisorsmay further include dieticians, chefs, and nutritionists who may offerexpertise around a user's diet and nutrition state and supplementation.One or more database tables in advisory database 1524 may include,without limitation alimentary professional informed advisors table 1712,which may include any and all information pertaining to alimentaryprofessional informed advisors. Alimentary professional informedadvisors may examine the fitness state of a user and may includepersonal trainers, coaches, group exercise instructors, and the like.One or more database tables in advisory database 1524 may include,without limitation a functional medicine informed advisors table 1720,which may include any and all information pertaining to functionalmedicine informed advisors. Functional medicine informed advisors mayinclude doctors, nurses, physician assistants, nurse practitioners andother members of the health care team. One or more database tables inadvisory database 1524 may include, without limitation a friends andfamily informed advisors table 1724, which may include any and allinformation pertaining to friends and family informed advisors. Friendsand family informed advisors may include friends and family members of auser who may create a positive community of support for a user. One ormore database tables in advisory database 1524 may include, withoutlimitation an electronic behavior coach informed advisor table 1728,which may include any and all information pertaining to electronicbehavior coach informed advisors. Electronic behavior coach informedadvisors may assist a user in achieving certain results such asmodifying behaviors to achieve a result such as assisting in additionrecovery and/or changing a user's eating habits to lose weight. One ormore database tables in advisory database 1524 may include withoutlimitation a miscellaneous informed advisor table 1732, which mayinclude any and all information pertaining to miscellaneous informedadvisors. Miscellaneous informed advisors may include any informedadvisors who do not fit into one of the categories such as for exampleinsurance coverage informed advisors. Miscellaneous informed advisortable 1732 may also contain miscellaneous information pertaining toinformed advisors such as a user's preference for informed advisors in acertain geographical location and/or other preferences for informedadvisors.

Referring now to FIG. 18, an exemplary embodiment of a default responsedatabase 1612 is illustrated. Default response database 1612 may beimplemented as any database and/or datastore suitable for use asbiological extraction database 300 as described above. One or moredatabase tables in default response database 1612 may include, withoutlimitation, an input/output table 1800, which may link default inputs todefault outputs. Default response database 1612 may include a user table1804, which may, for instance, map users and/or a user client device 180to particular user-specific learners and/or past conversations. Defaultresponse database 1612 may include a user preference table 1808 listingpreferred modes of address, turns of phrase, or other user-specificcommunication preferences. Default response database 1612 may include ageneral preference table 1812, which may track, for instance,output-input pairings associated with greater degrees of usersatisfaction.

Referring again to FIG. 16, artificial intelligence advisor may includea consultation initiator 1616 configured to detect a consultation eventin a user textual conversation and initiate a consultation with aninformed advisor as a function of the consultation event. A consultationevent, as used herein, is a situation where an informed advisor isneeded to address a user's situation or concerns, such as when a usershould be consulting with a doctor regarding an apparent medicalemergency or new condition, or with an advisor who can lend emotionalsupport when particularly distraught. Detection may be performed,without limitation, by matching an input and/or set of inputs to anoutput that constitutes an action of initiating a consultation; such apairing of an input and/or input set may be learned using a machinelearning process, for instance via general learner and/or user specificlearner 1608. In the latter case, information concerning a particularuser's physical or emotional needs or condition may be a part of thetraining set used to generate the input/input set to consultation eventpairing; for instance, a user with a history of heart disease maytrigger consultation events upon any inputs describing shortness ofbreath, chest discomfort, arrhythmia, or the like. Initiation ofconsultation may include transmitting a message to an advisor clientdevice 188 associated with an appropriate informed advisor, such aswithout limitation transmission of information regarding a potentialmedical emergency to a doctor able to assist in treating the emergency.Initiation of consultation may alternatively or additionally includeproviding an output to the user informing the user that a consultationwith an informed advisor, who may be specified by name or role, isadvisable.

Referring now to FIG. 19, an exemplary embodiment of alimentary module192 is illustrated. Alimentary module 192 may include any suitablehardware or software module. In an embodiment, alimentary module 192 isdesigned and configured to receive the at least an advisory output,select at least an informed advisor client device as a function of theat least a request for an advisory input. Alimentary module 192 includesuser communication learner 1600, which may be any form ofmachine-learning learner as described above in reference to FIGS. 1-18.User communication learner 1600 may include general learner 1604 whichmay derive relationships between user inputs and correct outputs using atraining set as described above in FIGS. 1-16. General learner 1604 maycorrelate inputs and outputs using conversations that may be logged by acomputing device 104. In an embodiment, general learner 1604 may use aninput such as a user request to consume a certain category of foods orsupplements to generate an output such as a specific alimentarycomponents and/or alimentary instructions. Inputs may be linked tocorresponding outputs for instance, by language processing module 128.For example, general learner 1604 may use an input from server 104 thatcontains a user preference to consume a specific type of food orsupplement to generate an output that includes a recommendation ofplant-based foods and/or bean/lentil-based foods. In yet anothernon-limiting example, general learner 1604 may use an input such as auser request to stabilize their metabolism or regulate their blood sugarto generate an output that includes an alimentary instruction thatincludes a minimum of three meals comprising whole-grain, higher-fiberfoods. User communication learner may include user-specific learner1608, that may generate inputs and outputs using any of themachine-learning methods as described above pertaining to a specificuser. In an embodiment, user-specific learner 1608 may utilizeuser-specific information contained within system 100 to generate inputsand outputs. For example, user-specific learner 1608 may utilize aninput such as a user request to consume a specific type of food inconjunction with user specific information such as user's history ofallergies to generate an output that includes an alimentary setcomprising foods that lack the allergens or immune triggers that mayfurther cause an adverse reaction to the user's immune system andinstead recommends foods that avoid the allergens altogether. In yetanother non-limiting example, user-specific learner 1608 may utilize aninput such as a user's request to consume a specific type of food inconjunction with user's past medical history of hypertension to generatean output that includes an alimentary set that includes foods and/oralimentary regimens that will both immediately and progressively reducethe user's blood pressure. Alimentary module 192 includes defaultresponse database 1612 which may link inputs to outputs according torelationships entered by users as described in more detail above inreference to FIG. 16. Default response database 1612 contain feedbackmechanisms to update inputs and outputs from subsequently generatedinstances from general learner 1604 and/or user-specific learner 1608.Inputs and outputs may be analyzed and updated into feedback mechanismsby learning processing module 128.

With continued reference to FIG. 19, alimentary module 192 may include aconsultation initiator 1616 configured to detect a consultation event ina user textual conversation such as by utilizing learning processingmodule 128 and initiate a consultation with an informed advisor such asan alimentary professional informed advisor. For example, a user textualconversation such as a complaint of blandness associated with foods in acurrent alimentary instruction set may initiate a consultation with analimentary professional informed advisor. A user textual conversationsuch as a complaint of frequently having to use the restroom due toconsumed foods may initiate a consultation with a functionalnutritionist informed advisor and/or an alimentary professional informedadvisor. Alimentary module 192 may include and/or communicate with analimentary informed advisor selector database 1904 as described in moredetail below in reference to FIG. 20. Alimentary module 192 may includea user category database 1908 as described in more detail below inreference to FIG. 21. Alimentary module 192 may transmit outputs such asat least an advisory output to user client device 180 and/or advisoryclient device 188.

With continued reference to FIG. 19, alimentary module 192 is configuredto receive a nutritional advisory intervention event and select aninformed advisor client device as a function of a nutritional advisoryintervention event. In an embodiment, alimentary module 192 may selectan informed advisor client device for an informed advisor that a userhas worked with in the past. In yet another non-limiting example,alimentary module 192 may select an informed advisor client device foran informed advisor client device that may be operated by an informedadvisor who practices a specialty and who may be able to assist a userbased on information contained within a nutritional advisoryintervention event. For example, a nutritional advisory interventionevent that indicates a user is having a hard time selecting a menu itemthat is compatible with a user's ketogenic diet, may require attentionby a dietician, while a question about ways to keep on track with healthgoals may be best suited to be answered by a health coach. Alimentarymodule may consult expert knowledge database to select an informedadvisor based on information contained within a nutritional advisoryintervention event.

Referring now to FIG. 20, an exemplary embodiment of alimentary informedadvisor selector database 1904 is illustrated. One or more databasetables in alimentary informed advisor selector database 1904 may link todata regarding an alimentary professional informed advisor. Alimentaryinformed advisor selector database 1904 may include one or more databasetables categorized by selection criteria to selected at least analimentary informed advisor. One or more database tables in alimentaryinformed advisor selector database 1904 may include, without limitation,user requested table 2002 (which may be communicatively linked orcoupled to user requested database 1908), which may include any and allinformation pertaining to user requests that may be utilized to selectat least an alimentary informed advisor, as described in more detailbelow in reference to FIG. 21. Alimentary informed advisor selectordatabase 1904 may include without limitation biological extractiondatabase 200, which may include any and all information pertaining tobiological extractions that may be utilized to select at least analimentary informed advisor. For example, a biological extraction suchas an elevated measurement of a user's percentage of body fat may beutilized to select an alimentary informed advisor who has receivedtraining and/or may be certified to aid a user in lowering an elevatedbody fat percentage via dietary or nutritional regiments. In yet anothernon-limiting example, a biological extraction such as an elevatedfasting blood glucose level may be utilized to select an alimentaryinformed advisor who may be a certified diabetes educator who may havereceived special training and instruction to assist a user in utilizingalimentary instructions to lower a fasting blood glucose level. One ormore database tables in alimentary informed advisor selector databasetable 1904 may include, without limitation, a diagnostic output databasetable 2004, which may include any and all information pertaining todiagnostic outputs that may be utilized to select at least an alimentaryinformed advisor. For example, a diagnostic output such as obesity maybe utilized to select an alimentary informed advisor who has receivedtraining and/or who may have experience working with users who haveobesity and associated co-morbid conditions that obese patientsfrequently experience such as diabetes, hypertension, heart disease,cerebrovascular disease, metabolic syndrome, sleep apnea, asthma,gastroesophageal reflux disease (GERD), polycystic ovary syndrome(PCOS), osteoarthritis and the like. In yet another non-limitingexample, diagnostic output database 2004 may be utilized for examplewhen a user receives a new diagnosis of hypertension, diagnostic outputdatabase table 2004 may be utilized to generate an advisory output thatincludes a recommendation and incorporation of foods into the user'sdiet regiment that support cardiovascular health at least three days aweek. In yet another non-limiting example, a user with a diagnosticoutput such as type 2 diabetes with a question about best food orsupplemental components to incorporate in their dietary regiment mayutilize diagnostic output database table 2004 to recommend complexcarbohydrates. One or more database tables in alimentary informedadvisor selector database 1904 may include, without limitation, anadvisory input database table 2008, which may include any and allinformation pertaining to advisory inputs that may be utilized toselected at least an alimentary informed advisor. For example, anadvisory input that includes a request for a recommendation by a user asto how to best prepare for an absolute fast or dry fasting may be linkedthrough advisory input database table 2008 to select at least analimentary informed advisor who has qualifications to advise users offoods necessary to participate in a fast without falling victim tohypoglycemia. In yet another non-limiting example, an advisory inputthat includes a question by a user as to how to best allocate foodproportions within their alimentary or dietary meal plan may be linkedthrough advisory input database table 2008 to an alimentary informedadvisor such as a nutritionist or dietician who may be knowledgeable asto best practices regarding meal prepping and portion control.

Referring now to FIG. 21, an exemplary embodiment of user requesteddatabase 1908 is illustrated. User requested database 1908 may includeone or more entries listing labels associated with alimentaryprofessional informed advisors. Linking may be performed by reference tohistorical data concerning alimentary professional informed advisorssuch as previous encounters and/or interactions with alimentaryprofessional informed advisors and/or services provided by an alimentaryprofessional informed advisor. One or more database tables may be linkedto one another by, for instance, common column values. Informed advisorsmay include any person besides the user who has access to informationuseable to aid user in interaction with system 100. Informed advisorsmay interact with one another and may aid user together in interactionwith artificial intelligence advisory support system. Alimentaryprofessional informed advisors may include any of the alimentaryprofessional informed advisors as described above, includingnutritionist, dieticians, meal planners and the like. Alimentaryprofessional informed advisor may provide output to user client device180 and/or advisory client device 188. Inputs and/or outputs may beexchanged using messaging services and/or protocols, including withoutlimitation any instant messaging protocols. Persons skilled in the art,upon review the entirety of this disclosure, will be aware of amultiplicity of communication protocols that may be employed to exchangetext messages as described herein. Text messages may be provided intextual form and/or as audio files using, without limitation,speech-to-text and/or text-to-speech algorithms. Informed advisors suchas alimentary professional informed advisors may provide inputs and/oroutputs to one another and/or to user. Alimentary professional informedadvisors may work together to create customized alimentary ornutritional plans around a user's fitness state, sugar levels, bloodpressure, or any other identifiable metric related to the user's health.Alimentary module 192 may select at least an informed advisor alimentaryprofessional client device as a function of a user-requested category ofat least an alimentary professional informed advisor. User-requestedcategory as used herein, includes a user request containing acharacteristic. Characteristic may include a feature or quality that auser requests in regard to at least an alimentary professional informedadvisor. Characteristics may include for example, a geographicalpreference as to where a user may meet with a particular alimentaryprofessional informed advisor and/or cuisine/food group specialty of aparticular alimentary professional informed advisor as described in moredetail below.

With continued reference to FIG. 21, user requested database 1908 mayinclude, without limitation, geographic location 2104 table, which maycontain any and all information pertaining to geographic location of atleast an alimentary professional informed advisor. Geographic locationmay include for example, a user's preference as to location of at leastan alimentary professional informed advisor. For example, a user wholives in Seattle, Wash. may prefer to work with an alimentaryprofessional informed advisor who is located in the greater Seattle areaincluding Bellevue, Redmond, and Renton due to the fact that thealimentary professional informed advisor would have a betterunderstanding of the alimentary or foods/supplements that the user hasaccess to based on geographic location. In such an instance, a user mayspecify locations for alimentary professional informed advisors withwhom user does not wish to work, because the locations are too far away.As a further non-limiting example, user may specify at least analimentary professional informed advisor that user does not wish to workwith because the alimentary professional informed advisor is notknowledgeable regarding resources accessible by the user. In yet anothernon-limiting example, a user who travels between several locations suchas for work or pleasure may provide input as to multiple geographicallocations at which the user wishes to work with an alimentaryprofessional informed advisor. For example, a user who resides inKentucky but travels to Tennessee one week each month for work mayprefer to work with an alimentary professional informed advisor inKentucky when user is in Kentucky and an alimentary professionalinformed advisor in Tennessee when user is in Tennessee.

Still referring to FIG. 21, user requested database 1908 may include,without limitation, dietary preference table 2108, which may contain anyand all information pertaining to dietary preferences of a user. Dietarypreference may include for example, a user's preference as to types offoods and supplemental components of at least an alimentary professionalinformed advisor. For example, a user who prefers an alimentaryprofessional informed advisor who specializes in establishinggluten-free alimentary instructions or meal plans due to the user livinga gluten-free lifestyle. In an embodiment, a user may not have apreference as to the type of foods or supplements of an alimentaryprofessional informed advisor and may request a full spectrum of typesand cuisines relating to foods and supplements. User requested database1908 may include, without limitation, alimentary preference database2112, which may contain any and all information pertaining to alimentarypreference of a user. Alimentary preference 2112 may include a user'sparticular likes, dislikes, intolerances, and reactions as to certaintypes of foods. For example, a user may provide information such as adislike for a certain palate such as spicy foods, but a fondness forfoods comprising a sweet or tangy taste. A user may provide a dislikefor a certain cuisines of food such as a dislike for Indian cuisine anda fondness for Mediterranean cuisine. A user may provide a dislike for acertain textures of food such as a dislike of foods with a grainytexture such as polenta or grits. A user may provide a fondness forfoods that comprise distinctive spices such as cilantro or ginger. Auser may provide a fondness or dislike for a certain type of means offood preparation such as a like for baked or broiled foods and a dislikefor fried foods. A user may provide a fondness or dislike for certaincategories of food such as seafood, meat, poultry, and the like. A usermay provide a fondness or dislike for foods inherently contained,served, or stored in certain industrial manners. For example, a user mayprefer chickens that are considered to be cage-free or a dislike offoods that have been previously frozen. User requested database 1908 mayinclude, without limitation, alimentary history database 2116, which maycontain any and all information pertaining to alimentary history of auser. Alimentary history may include previous dietary routines, allergicreactions, and/or meal plans that a user may have engaged in. Alimentaryhistory may include for example, previous chemical reactions orintolerances a user may have had to particular types of foods.Alimentary history may include previous alimentary instruction setsand/or meal plans associated with the user. Alimentary history mayinclude previous combinations of protein vs. carbohydrate vs. sugarintake that a user may have engaged in such as a ketogenic diet orpaleolithic diet that a user may have practiced.

With continued reference to FIG. 21, user requested database 1908 mayinclude, without limitation, previous informed advisors table 2120,which may contain any and all information pertaining to previousinformed advisors that user may have worked with. For example, previousinformed advisors table 2120 may contain a record of previousnutritionist, dieticians, and/or meal preparation providers that a usermay have worked with over a certain period of time. For example,previous informed advisors table 2120 may contain a record of user'snutrition coach that user has interacted with for six years. In yetanother non-limiting example, previous informed advisors table 2120 maycontain a record of a dietician that user had met with and trained withfor six sessions. User requested database 1908 may include, withoutlimitation, informed advisor certification table 2124, which may containany and all information pertaining to certifications of an informedadvisor. Certifications may include credentials such as educationalcertifications that an informed advisor may have achieved such as aBachelor of Science degree in biology, or a Bachelor of Science inchemistry. Certifications may include information such as a certainlevel of training that an informed advisor may have achieved such as anutritionist certification or dietician certification. Certificationsmay include a certification to assist a user in achieving personal bodygoals such as a certificate to become a food coach. Certifications mayinclude credentials to work with certain populations of users such as acertified diabetes educator or an obesity certification. User requesteddatabase 1908 may include, without limitation, religious preferencetable 2128, which may contain any and all information pertaining toreligious limitations and/or restraints on alimentary choices. Forexample, a certain sect or religion doctrine may prohibit the user fromeating pork. In yet another non-limiting example, a user may have apreference to eat at a certain faith-based diet such as a Daniel fast toimprove their physique and relationship with God. User requesteddatabase 1908 may include, without limitation, miscellaneous preferencetable 2132, which may contain any and all information pertaining to auser's preference for at least a alimentary professional informedadvisor that does not fit into any of the other databases containedwithin user requested database 1908. This may include for example, acertain time of day or day of the week that a user may prefer tointeract with an alimentary professional informed advisor. For example,a user who is an emergency room physician and works long and erratichours may have a preference to meet with an alimentary professional ondifferent days of the week or at different times of the day depending onuser's work schedule each week. table 2120.

Referring now to FIG. 22, an exemplary embodiment of a method 2200 of anartificial intelligence alimentary professional support network forvibrant constitutional guidance is illustrated. At step 2205 a computingdevice 104 and/or diagnostic engine 108 receives training data. Trainingdata includes a first training data set including a plurality of firstdata entries. Each first data entry of the first training set includesat least an element of physiological state data and at least acorrelated first prognostic label. In an embodiment, receiving the firsttraining set may include associating the at least an element ofphysiological state data with at least a category from a list ofsignificant categories of physiological state data. Categories ofphysiological state data may be received by an expert such as afunctional medicine practitioner. Training data includes a secondtraining data set including a plurality of second data entries, eachsecond data entry of the plurality of second data entries including atleast a second prognostic label and at least a correlated ameliorativeprocess label. In an embodiment, receiving second training set mayinclude associating at least a second prognostic label with at least acategory from a list of significant categories of prognostic labels.Receiving second training set may include associating at leastcorrelated ameliorative process label with at least a category from alist of significant categories of ameliorative process labels.Diagnostic engine receives at least a biological extraction from a user.Receiving at least a biological extraction from a user may includereceiving a physically extracted sample. This may include for example,receiving a blood sample of a user, a saliva sample, a DNA sample andthe like. Receiving at least a biological extraction may be implemented,without limitation, as described above in reference to FIGS. 1-21.

With continued reference to FIG. 22, at step 2210 a computing device 104and/or diagnostic engine 108 receives at least a biological extractionfrom a user; this may be performed, without limitation, as describedabove in reference to FIGS. 1-21. Diagnostic output includes aprognostic label and at least an ameliorative process label. Prognosticlabel may be generated by prognostic label learner 152 operating ondiagnostic engine 108. Prognostic label may be generated as a functionof the first training set and at least a biological extraction.Prognostic output may be generated by a lazy learning as a function ofthe first training set and the at least a biological extraction.

Still referring to FIG. 22, prognostic label may be generated by and/orusing at least a first machine-learning model 156 relating physiologicalstate data to prognostic labels. For example, one or more models maydetermine relationships between physiological state data and prognosticlabels. Relationships may include linear regression models and may bemodeled around relationships between physiological state data andcurrent prognostic labels. Prognostic output may be generated as afunction of a classification of the prognostic label. Prognostic labelsmay be categorized into different pairings and/or groupings as describedabove in reference to FIGS. 1-21. Machine-learning may examinerelationships between physiological state data and prognostic labels.Machine-learning algorithms may include any and all algorithms asperformed by any modules as described in this disclosure, includingwithout limitation algorithms described above regarding prognostic labellearner 152 and/or language processing module 128. For example,machine-learning may examine relationships between 25-hydroxy Vitamin Dlevels and current diagnosis of seasonal affective disorder (SAD).Machine-learning may examine relationships between physiological statedata such as a precursor condition and subsequent development of acondition, such as blood tests that are positive for varicella zostervirus (chicken pox) and subsequent diagnosis of herpes zoster, commonlyknown as shingles. Machine-learning models may examine relationshipsbetween current population of an individual's internal microbiome suchas presence of commensal Clostridioides difficile (C. difficile) andlater development and diagnosis of pathogenic C. difficile infection.Machine-learning models may examine relationships between currentpopulation of an individual's external microbiome such as presence ofcommensal Staphylococcus Aureus species and later development anddiagnosis of pathogenic infections such as Methicillin-ResistantStaphylococcus aureus (MRSA). Machine-learning models may examinerelationships between current physiological state and future developmentand diagnosis of a disease or condition such as the presence of BreastCancer Gene 1 (BRCA1) and/or Breast Cancer Gene 2 (BRCA2) and laterdevelopment and diagnosis of breast cancer and/or other cancers such asstomach cancer, pancreatic cancer, prostate cancer, and/or colon cancer.Machine-learning models may examine relationships between physiologicalstate data and diagnosed conditions such as triglyceride level, fastingglucose level, HDL cholesterol level, waist circumference, and/orsystolic blood pressure and later development and diagnosis of metabolicsyndrome. Machine-learning models may examine precursor state and rateof progression to diagnosis, such as appearance of drusen underneath theretina and/or angiography and rate of progression to diagnosis ofmacular degeneration. Machine-learning models may examine age of user atprecursor state and rate of progression to diagnosis, such as appearanceof drusen in a 20-year-old and subsequent age of diagnosis of maculardegeneration as compared to appearance of drusen in an 85-year-old andsubsequent age of diagnosis of macular degeneration. Machine-learningmodels may examine relationships between a plurality of prognosticlabels and root cause analysis, such as for example, prognostic labelsthat include presence of joint pain, limited mobility, elevated fastingglucose levels, and high body mass index (BMI) may indicate possiblelinkages to a root cause prognostic label of obesity. In yet anothernon-limiting example, a plurality of prognostic labels such as presenceof Acanthosis nigricans, elevated fasting blood sugar (blood glucoselevel greater than 100 mg/dL), endometrial hyperplasia, elevated bloodpressure (greater than 130 mmHg systolic and/or greater than 80 mmHgdiastolic), elevated total cholesterol levels (greater than 200 mg/dL),and elevated triglycerides (greater than 200 mg/dL) may indicatepossible linkages to a root cause prognostic label of Polycystic OvarianSyndrome (PCOS). Machine-learning models may examine correlations andrelationships between physiological state and overall mortality such asfor example measurement of telomeric DNA length and mortality. In yetanother non-limiting example, machine-learning models may examinetelomeric DNA length and subsequent diagnosis of diseases such ascardiovascular disease, diabetes, leukemia and the like.Machine-learning models may examine correlations and relationshipsbetween physiological state and severity and/or how rapidly a diseaseprogresses such as pancreatic cancer. Machine-learning models mayexamine factors such as age of onset and how rapidly a diseaseprogresses such as neurological diseases including for exampleAlzheimer's disease, Parkinson's disease, Bell's palsy, Lupus, stroke,rheumatoid arthritis, multiple sclerosis and the like. Prognostic labellearner 152 may generate prognostic output from prognostic label as afunction of the first training data set and at least a biologicalextraction. This may be done by any of the methodologies as describedabove. Prognostic output may be generated as a function of aclassification of prognostic label. This may be done by any of themethodologies as described above.

With continued reference to FIG. 19, a computing device 104 and/ordiagnostic engine 108 generates at least a diagnostic output include atleast an ameliorative process label; this may be performed, withoutlimitation, as described above in reference to FIGS. 1-21. Ameliorativeprocess label may be generated by ameliorative label learner 160operating on diagnostic engine 108. Ameliorative process label may begenerated as a function of the second training set and at least aprognostic output. Ameliorative process label may be generated by a lazylearning as a function of the second training set and at least anameliorative process descriptor.

With continued reference to FIG. 19, ameliorative process label learnermay generate the at least an ameliorative output by creating asecond-machine learning model using the second training set relatingprognostic labels to ameliorative labels. Second machine-learning modelmay use ameliorative label to generate at least an ameliorative output.Ameliorative output may be generated as a function of a classificationof the at least a prognostic output. Prognostic output may be classifiedby any schematic as described above in reference to FIGS. 1-21. Secondmachine learning-model may use models to create correlations relating aprognostic output such as high blood pressure to an ameliorative labelwith an alimentary recommendation that avoids salty foods so as toreduce high blood pressure. In yet another example, machine-learningmodels may create correlations relating a prognostic output such ascoronary artery occlusion to an ameliorative label such as angioplasty.Machine-learning models may group certain prognostic outputs to generateameliorative labels. For example, prognostic outputs that includedisease states associated with impaired fasting blood sugar such asdiabetes, polycystic ovarian syndrome, cardiovascular disease, metabolicsyndrome, and the like may be linked to an ameliorative label thatincludes an alimentary component configured to impair the disease. Inyet another non-limiting example, prognostic outputs that indicate arisk factor for cardiovascular disease such as uncontrolledhypertension, physical inactivity, obesity, uncontrolled diabetes,congenital heart disease, family history of heart disease, positivesmoking status, high cholesterol, high triglycerides, low HDL, and thelike may be linked to an ameliorative label that includes arecommendation to check 25-hydroxy vitamin D blood test. In yet anothernon-limiting example, machine-learning models may be utilized so that aprognostic output that is indicative of early aging such as shorttelomer length may be linked to an ameliorative label that includesanti-aging supplementation such as bioidentical hormone replacementtherapy, pregnenolone supplementation, resveratrol supplementation,coenzyme q10 supplementation and the like. Machine-learning models maybe utilized so that an ameliorative label may be linked to a prognosticoutput that includes a future risk of developing a disease or condition.For example, a prognostic output that includes a positive BRCA1diagnosis may be associated with an ameliorative label that includesdietary recommendations containing high consumption of cruciferousvegetables. In yet another non-limiting example, a prognostic outputthat includes a positive presence of commensal C. difficile may beassociated with an ameliorative label that includes recommendations tosupplement with Saccharomyces boulardii. Ameliorative output may begenerated as a function of the second training data set and the at leasta prognostic output. This may be performed by any of the methodologiesas described above. Ameliorative output may be generated a function of aclassification of the at least a prognostic output. This may beperformed by any of the methodologies as described above.

With continued reference to FIG. 22, at step 2215 a computing device 104and/or at least an advisory module operating on a computing devicereceives at least a request for an advisory input and generates adiagnostic input; this may be performed, without limitation, asdescribed above in reference to FIGS. 1-21. Receiving at least a requestfor an advisory input and generating a diagnostic input may beimplemented, without limitation, as described above in FIGS. 1-21. Atleast a request for an advisory input may be received from user clientdevice 180, advisor client device 188, informed advisor, diagnosticoutput, and/or artificial intelligence advisor 1508 as described in moredetail above in FIGS. 1-21.

With continued reference to FIG. 22, at step 2220 a computing device 104and/or advisory module operating on the a computing device generates atleast an advisory output using the at least a request for an advisoryinput and at least a diagnostic output; this may be performed, withoutlimitation, as described above in reference to FIGS. 1-21. Languageprocessing module 128 may evaluate at least a request for an advisoryinput and extract one or more words. For example, language processingmodule 128 may evaluate at least a request for an advisory input thatcontains words pertaining to diet or nutrition such as “calories,intake, carbohydrates, serving size, total fat, saturated fat, transfat, sodium, and protein.” Language processing module 128 may evaluateat least a request for an advisory input and extract one or more wordspertaining to what specialty of alimentary professional informed advisormay be necessary. For example, language processing module 128 mayevaluate at least a request for an advisory input that contains acomplaint of acid reflux and may warrant the attention of a dietician ascompared to at least a request for an advisory input that contains arequest for an explanation of how to reduce fat intake. Languageprocessing module 128 may evaluate at least a request for an advisoryinput and extract one or more words pertaining to other informedadvisors that may be necessary either in lieu of an alimentaryprofessional informed advisor and/or in addition to an alimentaryprofessional informed advisor. For example, at least a request for anadvisory input may contain a question or remark that includes both dietand nutrition recommendations. In such an instance, language processingmodule 128 may extract one or more words pertaining to nutrition andalimentary professional informed advisors. In an embodiment, languageprocessing module 128 may evaluate at least a request for an advisoryinput that may warrant the attention of other informed advisors. Forexample, at least a request for an advisory input may include acomplaint of intolerance of a specific food that may warrant theattention of an informed advisor such as a functional medicine doctor.

With continued reference to FIG. 22, advisory module operating on the acomputing device may generate at least an advisory output using the atleast a request for an advisory input and at least a diagnostic output;this may be performed, without limitation, as described above inreference to FIGS. 1-21. Advisory output may include for example,specialized instruction set 1504, textual entry field 1512, categoryfield 1516, and/or relevance field 1520 as described in more detailabove in reference to FIG. 15. Any of the textual fields may allow forexample an informed advisor to browse to a table of contents to findpertinent information such as a certain test result or results from aprocedure that were obtained as described in more detail above in FIG.15. Textual fields may allow an informed advisor to have an advisoryoutput open to most relevant results, such as an endocrinologist who maybe interested in relevant results such as blood sugar measurements andfasting glucose levels. Textual fields may allow an informed advisor togenerate an advisory output to another informed advisor containinginformation of relevance. For example, a functional medicine doctor mayshare relevant information surrounding a user's dramatic drop in bodyfat percentage with friends and family for a user suffering withanorexia. This may include information such as a user's response to amedication and/or supplement to treat user's anorexia. Textual fieldsmay be implemented and may include any of the textual fields asdescribed above in reference to FIG. 15.

With continued reference to FIG. 22, at step 2225 a computing device 104and/or alimentary module operating on the a computing device selects atleast an informed advisor client device as a function of the at least arequest for an advisory input; this may be performed, withoutlimitation, as described above in reference to FIGS. 1-21. Selecting atleast an informed advisor client device may include matching the atleast a request for an advisory input to the at least an informedadvisor. Matching may include for example, matching an input to anoutput that constitutes a specific alimentary professional informedadvisor. Matching may be learned using a machine learning process, forinstance via general learner 1804 and/or user specific learner 1808. Forexample, information concerning a particular request for an advisoryinput may be part of a training set used to generate matching algorithmsbetween at least a request for an advisory input and selecting at leastan alimentary professional informed advisor. For example, at least arequest for an advisory input that contains a question pertaining tobest foods for a user with obesity may be matched to an alimentaryprofessional informed advisor who has experience working with obesepatients. At least a request for an advisory input containing certain“buzz word” may be matched to at least a specific alimentaryprofessional informed advisor that such buzzwords may be associatedwith. For example, at least a request for an advisory input may beanalyzed by language processing module 128 for words such as“nourishment, subsistence, dietetics, alimentary instruction set, mealplan, menu, victuals” may be matched to at least an alimentaryprofessional such as a dietician. In yet another non-limiting example,at least a request for an advisory input analyzed by language processingmodule 128 that contains words such as “low-fat, low-carb, high-protein,vegan, pescatarian” may be matched to at least an alimentaryprofessional such as a certified nutritionist.

With continued reference to FIG. 22, at step 2230 a computing device 104and/or alimentary module operating on the a computing device transmitsat least an advisory output to at least an informed advisor alimentaryprofessional client device; this may be performed, without limitation,as described above in reference to FIGS. 1-21.

With continued reference to FIG. 22, matching may be performed takinginto account user requested preferences for at least an alimentaryprofessional informed advisor. Alimentary module 192 may consult any andall information contained within user requested database 1908. Userrequested database 1908 may contain user preferences as to an alimentaryprofessional informed advisor as described in more detail above inreference to FIG. 21. For example, at least a request for an advisoryinput containing a consultation with a nutritionist may be matched to anutritionist located within user's requested geographic location. Atleast a request for an advisory input containing a user request to workwith an alimentary professional informed advisor user previously workedwith may be matched to that specific alimentary professional informedadvisor user previously worked with. Matching may be performed byalimentary module 192 utilizing biological extraction database 200.Matching may be learned through a machine-learning process that utilizesinputs of biological extractions and matches them to outputs containingalimentary professional informed advisors. For example, a biologicalextraction such as an elevated percentage body fat may be matched to analimentary professional informed advisor who has received specializedtrainings and/or certifications to know how to safely lower elevatedbody fat. In yet another non-limiting example, a biological extractionsuch as a high cholesterol measurement indicating liver or kidneydisease may be matched to an alimentary professional informed advisorspecializing in cholesterol management. Matching may be performed byalimentary module 192 utilizing diagnostic output database 2004.Matching may be learned through a machine-learning process that utilizesinputs of diagnostic outputs and matches them to outputs containingalimentary professional informed advisors. For example, a diagnosticoutput such as Type 2 Diabetes Mellitus may be matched to an alimentaryprofessional informed advisor such as a dietician to utilize caloricintake as a way to lower elevated fasting blood sugars. In yet anothernon-limiting example, a diagnostic output such as exhaustion may bematched to an alimentary professional informed advisor such as anutritionist who may advise the user about meals that prevent shuttingdown the functioning of the brain and the body. In an embodiment,diagnostic output such as hypertension may be matched to an alimentaryprofessional informed advisor such as a nutritionist who may utilizemeal plans that completely avoid sodium-intake.

With continued reference to FIG. 22, at step 2330 a computing deviceand/or alimentary module operating on the a computing device transmitsthe at least an advisory output to the at least an informed advisorclient device; this may be performed, without limitation, as describedabove in reference to FIGS. 1-21. In an embodiment, transmission mayinclude transmitting the at least an advisory output to a user clientdevice and/or an advisor client device. Transmission may includetransmitting the at least an advisor output to a client-interfacemodule. Transmission may be implemented, without limitation, asdescribed above in reference to FIGS. 1-21.

Referring now to FIG. 23, an exemplary embodiment of a method 2300 of anartificial intelligence alimentary professional support network forvibrant constitutional guidance is illustrated. At step 2305, acomputing device 104 receives a first training data set including aplurality of first data entries, each first data entry of the pluralityof first data entries including at least an element of physiologicalstate data and at least a correlated first prognostic label. This may beperformed utilizing any of the methodology as described above in moredetail in reference to FIGS. 1-22.

With continued reference to FIG. 23, at step 2310, a computing device104 receives a second training data set including a plurality of seconddata entries, each second data entry of the plurality of second dataentries including at least a second prognostic label and at least acorrelated ameliorative process label. This may be performed utilizingany of the methodology as described above in more detail in reference toFIGS. 1-22.

With continued reference to FIG. 23, at step 2315, a computing device104 receives a biological extraction from a user. A biologicalextraction includes any of the biological extractions as described abovein more detail in reference to FIGS. 1-22.

With continued reference to FIG. 23, at step 2320, a computing device104 generates a diagnostic output based on a first training set, asecond training set, and a biological extraction. This may be performedutilizing any of the methodologies as described above in more detail inreference to FIGS. 1-22.

With continued reference to FIG. 23, at step 2325, a computing device104 detects a nutritional advisory intervention event as a function of adiagnostic output. A nutritional advisory intervention event includesany of the nutritional advisory intervention events as described abovein more detail in reference to FIGS. 1-22. In an embodiment, computingdevice 104 may detect a nutritional advisory intervention eventutilizing a diagnostic output and input contained within expertdatabase. Computing device 104 may detect a nutritional advisoryintervention event as a function of a biological extraction. Forexample, computing device 104 may retrieve one or more biologicalextractions from biological extraction database 200 and may evaluate thebiological extractions to determine if they fall within normal limits,as described above in more detail in reference to FIG. 1. In anembodiment, if a biological extraction does not fall within normallimits, computing device 104 may detect a nutritional advisoryintervention event, as described above in more detail. Computing device104 may detect a nutritional advisory intervention event as a functionof an input from a user client device. For example, a friend, familymember, and/or the user may report a nutritional advisory interventionevent, such as when a user has not been compliant with a style ofeating, or when the user has a question about what food a user shouldorder off a menu at a restaurant. Computing device 104 may detect anutritional advisory intervention event utilizing a user-specificlearner. This may be performed utilizing any of the methods as describedabove in more detail in reference to FIGS. 1-23. In an embodiment,user-specific learner may receive a plurality of nutritional inputsidentifying user nutritional behavior. Inputs identifying usernutritional behavior may include any of the inputs as described above inmore detail in reference to FIGS. 1-23. Computing device 104 identifiesa nutritional behavior outlier, which may include any behavior that doesnot fall within the user's average and/or general behaviors, asdescribed above in more detail. Computing device 104 initiates aconsultation event with a nutritional informed advisor as describedabove in more detail. A nutritional informed advisor includes any of theinformed advisors as described above in more detail.

With continued reference to FIG. 23, at step 2330, a computing device104 generates a response to an advisory intervention event wherein theresponse identifies an advisory action. A response, includes any of theresponses as described above in more detail. Computing device 104 maygenerate a response that adjusts a comprehensive instruction set. Forexample, computing device 104 may generate a response that contains newinstructions for a new diet the user can implement, upon determiningthat a user was not compliant with a first diet. In yet anothernon-limiting example, computing device 104 may generate a response thatinstructs a user to consume additional foods that may boost a user's lowhigh density lipoprotein (HDL) including avocado, fish oil, salmon, andwalnuts. Computing device 104 may generate a response that contains atextual output. Textual output may include any of the textual outputs asdescribed above. Textual output may include words of encouragement,support, and/or engagement for a user. For example, textual output mayremind a user not to consume sugary foods if the user has a diagnosticoutput indicating the user has diabetes. In yet another non-limitingexample, textual output may remind a user with coronary artery diseaseto consume foods rich in fiber including bran, flax seeds, and prunes.Computing device 104 is configured to generate a response that containsan urgency label to indicate an emergency situation. Computing device104 locates an emergency nutritional informed advisor and transmits theresponse to an advisor client device operated by the emergencynutritional informed advisor.

With continued reference to FIG. 23, at step 2335, a computing device104 transmits the response to the user client device. Computing device104 may transmit the response to the user client device utilizing anynetwork methodology as described herein.

Systems and methods described herein provide improvements to themaintenance and functioning of alimentary professional support networksby utilizing artificial intelligence in order to provide alimentarysuggestions and advice based on analyses derived from user extractions,and other collected data such as user inputs; systems and methodsdescribe herein may furnish alimentary suggestions, advice, and/or othercommunications in real time. Use of this data facilitates the alimentaryprofessional support network by allowing automated decisions andperformances associated with a user, their supportive resources, andtheir alimentary lifestyle to be rendered in a manner that providesvibrant constitutional guidance. Furthermore, systems and methodsdescribed in this disclosure provide an unconventional use of theplurality of collected data via automatic execution of processes andperformances by the vibrant constitutional advice network based on thealimentary professional support network and the plurality of collecteddata. Thus, the systems and methods described herein improve thefunctioning of computing systems by optimizing big data processing andimproving the utility of the processed big data via its unconventionalapplication.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 24 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 2400 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 2400 includes a processor 2404 and a memory2408 that communicate with each other, and with other components, via abus 2412. Bus 2412 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Memory 2408 may include various components (e.g., machine-readablemedia) including, but not limited to, a random-access memory component,a read only component, and any combinations thereof. In one example, abasic input/output system 2416 (BIOS), including basic routines thathelp to transfer information between elements within computer system2400, such as during start-up, may be stored in memory 2408. Memory 2408may also include (e.g., stored on one or more machine-readable media)instructions (e.g., software) 2420 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 2408 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 2400 may also include a storage device 2424. Examples ofa storage device (e.g., storage device 2424) include, but are notlimited to, a hard disk drive, a magnetic disk drive, an optical discdrive in combination with an optical medium, a solid-state memorydevice, and any combinations thereof. Storage device 2424 may beconnected to bus 2412 by an appropriate interface (not shown). Exampleinterfaces include, but are not limited to, SCSI, advanced technologyattachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394(FIREWIRE), and any combinations thereof. In one example, storage device2424 (or one or more components thereof) may be removably interfacedwith computer system 2400 (e.g., via an external port connector (notshown)). Particularly, storage device 2424 and an associatedmachine-readable medium 2428 may provide nonvolatile and/or volatilestorage of machine-readable instructions, data structures, programmodules, and/or other data for computer system 2400. In one example,software 2420 may reside, completely or partially, withinmachine-readable medium 2428. In another example, software 2420 mayreside, completely or partially, within processor 2404.

Computer system 2400 may also include an input device 2432. In oneexample, a user of computer system 2400 may enter commands and/or otherinformation into computer system 2400 via input device 2432. Examples ofan input device 2432 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 2432may be interfaced to bus 2412 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 2412, and any combinations thereof. Input device 2432may include a touch screen interface that may be a part of or separatefrom display 2436, discussed further below. Input device 2432 may beutilized as a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 2400 via storage device 2424 (e.g., a removable disk drive, aflash drive, etc.) and/or network interface device 2440. A networkinterface device, such as network interface device 2440, may be utilizedfor connecting computer system 2400 to one or more of a variety ofnetworks, such as network 2444, and one or more remote devices 2448connected thereto. Examples of a network interface device include, butare not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A network,such as network 2444, may employ a wired and/or a wireless mode ofcommunication. In general, any network topology may be used. Information(e.g., data, software 2420, etc.) may be communicated to and/or fromcomputer system 2400 via network interface device 2440.

Computer system 2400 may further include a video display adapter 2452for communicating a displayable image to a display device, such asdisplay device 2436. Examples of a display device include, but are notlimited to, a liquid crystal display (LCD), a cathode ray tube (CRT), aplasma display, a light emitting diode (LED) display, and anycombinations thereof. Display adapter 2452 and display device 2436 maybe utilized in combination with processor 2404 to provide graphicalrepresentations of aspects of the present disclosure. In addition to adisplay device, computer system 2400 may include one or more otherperipheral output devices including, but not limited to, an audiospeaker, a printer, and any combinations thereof. Such peripheral outputdevices may be connected to bus 2412 via a peripheral interface 2456.Examples of a peripheral interface include, but are not limited to, aserial port, a USB connection, a FIREWIRE connection, a parallelconnection, and any combinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A system for an artificial intelligencealimentary professional support network for vibrant constitutionalguidance, the system comprising: a computing device; at least adiagnostic engine operating on the computing device, the at least adiagnostic engine designed and configured to: receive a first trainingdata set including a plurality of first data entries, each first dataentry of the plurality of first data entries including at least anelement of physiological state data and at least a correlated firstprognostic label; receive a second training data set including aplurality of second data entries, each second data entry of theplurality of second data entries including at least a second prognosticlabel and at least a correlated ameliorative process label; train,iteratively, a first machine-learning model using the first trainingdata set and a first machine-learning process; train, iteratively, asecond machine-learning model using the second training data set and asecond machine-learning process; receive at least a biologicalextraction from a user; and generate a diagnostic output based on the atleast a biological extraction and the first machine-learning model,wherein the first machine-learning model uses the at least a biologicalextraction as an input to output the diagnostic output; an advisorymodule designed and configured to: detect a nutritional advisoryintervention event as a function of the diagnostic output; generate aresponse to the advisory intervention event wherein the responseidentifies an advisory action; and transmit the response to a userclient device.
 2. The system of claim 1, wherein the computing devicefurther comprises: an alimentary input module designed and configuredto: receive the nutritional advisory intervention event; and select atleast an informed advisor client device as a function of the nutritionaladvisory intervention event.
 3. The system of claim 2, wherein thealimentary input module selects the at least an informed advisor clientdevice as a function of the at least a biological extraction.
 4. Thesystem of claim 1, wherein the advisory action comprises instructionsfor a diet.
 5. The system of claim 1, wherein the advisory module isfurther configured to detect the nutritional advisory intervention eventas a function of an input from the user client device.
 6. The system ofclaim 1, wherein the advisory module is further configured to detect thenutritional advisory intervention event utilizing a user-specificlearner.
 7. The system of claim 6, the user-specific learner is furtherconfigured to: receive a plurality of nutritional inputs identifyinguser nutritional behavior; identify a nutritional behavior outlier; andinitiate a consultation event with a nutritional informed advisor. 8.The system of 1, wherein the advisory module is further configured togenerate the advisory action adjusting a comprehensive instruction set.9. The system of claim 1, wherein the advisory module is furtherconfigured to generate the advisory action containing a textual output.10. The system of claim 1, wherein the advisory module is furtherconfigured to: generate the response to contain an urgency label; locatean emergency nutritional informed advisor; and transmit the response toan advisor client device operated by the emergency nutritional informedadvisor.
 11. A method of an artificial intelligence alimentaryprofessional support network for vibrant constitutional guidance, themethod comprising: receiving by a computing device a first training dataset including a plurality of first data entries, each first data entryof the plurality of first data entries including at least an element ofphysiological state data and at least a correlated first prognosticlabel; receiving by the computing device a second training data setincluding a plurality of second data entries, each second data entry ofthe plurality of second data entries including at least a secondprognostic label and at least a correlated ameliorative process label;train, iteratively, a first machine-learning model using the firsttraining data set and a first machine-learning process; train,iteratively, a second machine-learning model using the second trainingdata set and a second machine-learning process; receiving by thecomputing device at least a biological extraction from a user;generating by the computing device a diagnostic output based the atleast a biological extraction and the first machine-learning model,wherein the first machine-learning model uses the at least a biologicalextraction as an input to output the diagnostic output; detecting by thecomputing device a nutritional advisory intervention event as a functionof the diagnostic output; generating by the computing device a responseto the advisory intervention event wherein the response identifies anadvisory action; and transmitting by the computing device the responseto a user client device.
 12. The method of claim 11, further comprising:receiving the nutritional advisory intervention event; and selecting atleast an informed advisor client device as a function of the nutritionaladvisory intervention event.
 13. The method of claim 12, wherein the atleast an informed advisor client device is selected as a function of theat least a biological extraction.
 14. The method of claim 11, whereinthe advisory action comprises instructions for a diet.
 15. The method ofclaim 11, wherein the computing device detects the nutritional advisoryintervention event as a function of an input from a user client device.16. The method of claim 11, wherein the computing device detects thenutritional advisory intervention event utilizing a user-specificlearner.
 17. The method of claim 16 further comprising: receiving aplurality of nutritional inputs identifying user nutritional behavior;identifying a nutritional behavior outlier; and initiating aconsultation event with a nutritional informed advisor.
 18. The methodof 11, wherein generating the advisory action further comprisesadjusting a comprehensive instruction set.
 19. The method of claim 11,wherein generating the advisory action further comprises generating atextual output.
 20. The method of claim 11 further comprising:generating the response to contain an urgency label; locating anemergency nutritional informed advisor; and transmitting the response toan advisor client device operated by the emergency nutritional informedadvisor.